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Decoding the key dynamical processes that shape the Galactic disk structure is crucial for reconstructing the Milky Way's evolution history. The second Gaia data release unveils a novel wave pattern in the $L_Z-\langle V_R\rangle$ space, but its formation mechanism remains elusive due to the intricate nature of involved perturbations and the challenges in disentangling their effects. Utilizing the latest Gaia DR3 data, we find that the $L_Z-\langle V_R\rangle$ wave systematically shifts towards lower $L_Z$ for dynamically hotter stars. The amplitude of this phase shift between stars of different dynamical hotness ($\Delta L_Z$) peaks around $\mathrm{2300\,km\,s^{-1}\,kpc}$. To differentiate the role of different perturbations, we perform three sets of test particle simulations, wherein a satellite galaxy, corotating transient spiral arms, and a bar plus the corotating transient spiral arms act as the sole perturber, respectively. Under the satellite impact, the phase shift amplitude decreases towards higher $L_Z$, which we interpret through a toy model of radial phase mixing. While the corotating transient spiral arms do not generate an azimuthally universal phase shift variation pattern, combining the bar and spirals generates a characteristic $\Delta L_Z$ peak at 2:1 Outer Lindblad Resonance, qualitatively resembling the observed feature. Therefore, the $L_Z-\langle V_R\rangle$ is more likely of internal origin. Furthermore, linking the $\Delta L_Z$ peak to the 2:1 Lindblad resonance offers a novel approach to estimating the pattern speed of the Galactic Bar, supporting the long/slow bar model., Comment: Submitted to ApJ, 13 pages, 8 figures, comments welcome

We perform a comprehensive investigation of the PSF smoothing effect on the measurement of concentration-related parameters ($C$, Gini, $M_{20}$) of high redshift galaxies in the HST and JWST surveys. Our sample contains massive galaxies from the CANDELS/EGS survey (0 < z < 2), and the CEERS survey (1 < z < 3). The non-parametric concentration-related parameters ($C$, Gini, $M_{20}$) and the model-dependent parameters (n, Re) of these galaxies are derived from Statmorph and GALFIT, respectively. We try to evaluate the PSF smoothing effect by comparing the concentration-related parameters to the S\'{e}rsic index in both observations and mock images. We find that the concentration index is generally underestimated, especially for smaller galaxies with higher S\'{e}rsic index (eventually converging to the concentration index of the PSF). However, galaxies with lower S\'{e}rsic index ($n \leq 1$) or larger relative size are less affected by the PSF smoothing effect. The Gini coefficient and the absolute $M_{20}$ statistic also show similar behaviour as the concentration index. Caution should be taken for the possible correction of the concentration-related parameters, where both the relative size and the S\'{e}rsic index of the galaxy are important. Compared to the HST images, the PSF smoothing is much less severe for images in the CEERS survey due to the much higher spatial resolution. In fact, it is better to use the S\'{e}rsic index rather than the non-parametric morphology indicators to trace the light concentration for galaxies at high redshifts. From the single S\'{e}rsic modelling of the HST and JWST images, we also confirm that galaxies at higher redshift are more compact with smaller $R_e$. The lower mass galaxies are more disc-like ($n\sim1$) compared to the higher mass galaxies that are more spheroid dominated ($n\sim3$)., Comment: Submitted to A&A, 21 pages, 20 figures. Comments are welcome

Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph. However, most methods require a labelled source graph to provide supervision signals, which might not be accessible in the real-world settings due to regulations and privacy concerns. In this paper, we explore the scenario of source-free unsupervised graph domain adaptation, which tries to address the domain adaptation problem without accessing the labelled source graph. Specifically, we present a novel paradigm called GraphCTA, which performs model adaptation and graph adaptation collaboratively through a series of procedures: (1) conduct model adaptation based on node's neighborhood predictions in target graph considering both local and global information; (2) perform graph adaptation by updating graph structure and node attributes via neighborhood contrastive learning; and (3) the updated graph serves as an input to facilitate the subsequent iteration of model adaptation, thereby establishing a collaborative loop between model adaptation and graph adaptation. Comprehensive experiments are conducted on various public datasets. The experimental results demonstrate that our proposed model outperforms recent source-free baselines by large margins., Comment: Accepted by WWW-2024

Existing knowledge hypergraph embedding methods mainly focused on improving model performance, but their model structures are becoming more complex and redundant. Furthermore, due to the inherent complex semantic knowledge, the computation of knowledge hypergraph embedding models is often very expensive, leading to low efficiency. In this paper, we propose a feature interaction and extraction-enhanced 3D circular convolutional embedding model, HyCubE, which designs a novel 3D circular convolutional neural network and introduces the alternate mask stack strategy to achieve efficient n-ary knowledge hypergraph embedding. By adaptively adjusting the 3D circular convolution kernel size and uniformly embedding the entity position information, HyCubE improves the model performance with fewer parameters and reaches a better trade-off between model performance and efficiency. In addition, we use 1-N multilinear scoring based on the entity mask mechanism to further accelerate the model training efficiency. Finally, extensive experimental results on all datasets demonstrate that HyCubE consistently outperforms state-of-the-art baselines, with an average improvement of 4.08%-10.77% and a maximum improvement of 21.16% across all metrics. Commendably, HyCubE speeds up by an average of 7.55x and reduces memory usage by an average of 77.02% compared to the latest state-of-the-art baselines., Comment: 11 pages, 5 figures

This study aims to investigate the strong gravitational lensing effects in $f(T)$ gravity. We present the theoretical analytic expressions for the lensing effects in $f(T)$ gravity, including deflection angle, magnification, and time delay. On this basis, we also take the plasma lensing effect into consideration. We compare the lensing effects between the General Relativity in a vacuum environment and the $f(T)$ gravity in a plasma environment. From a strongly lensed fast radio burst, the results indicate that in a plasma environment, General Relativity and $f(T)$ gravity can generate indistinguishable image positions, but the magnification and time delay on these positions are significantly different, which can be distinguished by current facilities in principle. Therefore, the discrepancies between observational results and theoretical expectations can serve as clues for a modified gravity theory and provide constraints on $f(T)$ gravity.

In this paper, we construct the Dirac stars model composed of two Dirac fields and Einstein gravity in four-dimensional Anti-de Sitter spacetime. We consider the Dirac stars with free field (no self-interacting). Our investigation focuses on understanding the relationship between Arnowitt-Deser-Misner (ADM) mass, Noether charge, and binding energy in relation to the cosmological constant. Furthermore, we extend the study to the Dirac stars with self-interacting potentials. For the self-interacting Dirac stars, three types of interactions are examined: only quartic, only sextic, quartic and sextic interactions that is kind of Q-ball type interactions. In these three distinct scenarios, it is essential to investigate the impact of self-interaction constants on Dirac stars. Additionally, we study the Dirac Q-balls in the AdS background., Comment: 17pages, 27pictures

Knowledge graphs generally suffer from incompleteness, which can be alleviated by completing the missing information. Deep knowledge convolutional embedding models based on neural networks are currently popular methods for knowledge graph completion. However, most existing methods use external convolution kernels and traditional plain convolution processes, which limits the feature interaction capability of the model. In this paper, we propose a novel dynamic convolutional embedding model ConvD for knowledge graph completion, which directly reshapes the relation embeddings into multiple internal convolution kernels to improve the external convolution kernels of the traditional convolutional embedding model. The internal convolution kernels can effectively augment the feature interaction between the relation embeddings and entity embeddings, thus enhancing the model embedding performance. Moreover, we design a priori knowledge-optimized attention mechanism, which can assign different contribution weight coefficients to multiple relation convolution kernels for dynamic convolution to improve the expressiveness of the model further. Extensive experiments on various datasets show that our proposed model consistently outperforms the state-of-the-art baseline methods, with average improvements ranging from 11.30\% to 16.92\% across all model evaluation metrics. Ablation experiments verify the effectiveness of each component module of the ConvD model.

Unsupervised graph anomaly detection is crucial for various practical applications as it aims to identify anomalies in a graph that exhibit rare patterns deviating significantly from the majority of nodes. Recent advancements have utilized Graph Neural Networks (GNNs) to learn high-quality node representations for anomaly detection by aggregating information from neighborhoods. However, the presence of anomalies may render the observed neighborhood unreliable and result in misleading information aggregation for node representation learning. Selecting the proper neighborhood is critical for graph anomaly detection but also challenging due to the absence of anomaly-oriented guidance and the interdependence with representation learning. To address these issues, we utilize the advantages of reinforcement learning in adaptively learning in complex environments and propose a novel method that incorporates Reinforcement neighborhood selection for unsupervised graph ANomaly Detection (RAND). RAND begins by enriching the candidate neighbor pool of the given central node with multiple types of indirect neighbors. Next, RAND designs a tailored reinforcement anomaly evaluation module to assess the reliability and reward of considering the given neighbor. Finally, RAND selects the most reliable subset of neighbors based on these rewards and introduces an anomaly-aware aggregator to amplify messages from reliable neighbors while diminishing messages from unreliable ones. Extensive experiments on both three synthetic and two real-world datasets demonstrate that RAND outperforms the state-of-the-art methods., Comment: 1O pages, 7 figures, accepted by ICDM2023

Next Basket Recommender Systems (NBRs) function to recommend the subsequent shopping baskets for users through the modeling of their preferences derived from purchase history, typically manifested as a sequence of historical baskets. Given their widespread applicability in the E-commerce industry, investigations into NBRs have garnered increased attention in recent years. Despite the proliferation of diverse NBR methodologies, a substantial challenge lies in the absence of a systematic and unified evaluation framework across these methodologies. Various studies frequently appraise NBR approaches using disparate datasets and diverse experimental settings, impeding a fair and effective comparative assessment of methodological performance. To bridge this gap, this study undertakes a systematic empirical inquiry into NBRs, reviewing seminal works within the domain and scrutinizing their respective merits and drawbacks. Subsequently, we implement designated NBR algorithms on uniform datasets, employing consistent experimental configurations, and assess their performances via identical metrics. This methodological rigor establishes a cohesive framework for the impartial evaluation of diverse NBR approaches. It is anticipated that this study will furnish a robust foundation and serve as a pivotal reference for forthcoming research endeavors in this dynamic field., Comment: 6 pages, 2 figures

The vertical phase-space spiral (snail) is a direct sign of dis-equilibrium of Milky Way's disc. Nevertheless, the wrapping of the phase snail contains information of the vertical potential. We propose a novel method to measure the vertical potential utilizing the intersections between the snail and $z$/$V_{z}$ axes, for which we know the maximum vertical heights ($Z_{max}$) or the maximum vertical velocities ($V_{z,max}$). Using a refined linear interpolation method, we directly obtain $(Z_{max},\ \frac{1}{2}V_{z,max}^{2})$ for these snail intersections to constrain the vertical potential profile empirically. Our method is model independent since no assumptions about the snail shape or the vertical potential have been made. Although the snail binned by the guiding center radius ($R_{g}$) is more prominent, it traces a vertical potential shallower than that of the snail binned by the same Galactocentric radius ($R$). We apply an empirical method to correct this difference. We measure the snail intersections in several $R_{g}$ bins within $7.5< R_{g} < 11.0$ kpc for Gaia DR3, and apply the interpolation method to deduce the potential values at several vertical heights. The potential at the snail intersections, as well as the following mass modeling are consistent with the popular Milky Way potentials in the literature. For the $R_{g}$-binned phase snail in the Solar neighborhood, the mass modeling indicates a local dark matter density of $\rho_{\rm dm}= 0.0150\pm0.0031$ $\rm M_{\odot}\,pc^{-3}$, consistent with previous works. Our method could be applied to larger radial ranges in future works, to provide independent and stronger constraints on the Milky Way's potential., Comment: Accepted for publication on ApJ, 25 pages, 15 figures

FeAmGen.jl is a Julia package designed to generate Feynman diagrams and their corresponding amplitudes for various processes in particle physics. Utilizing the models in the Universal Feynman Output (UFO) format and Qgraf for diagram generation, it also employs SymEngine.jl and Form for amplitude generation. Additionally, the package offers functions for constructing Feynman integral topologies. In conclusion, FeAmGen.jl provides usability and versatility for the high precision calculations of the perturbative quantum field theory in the Standard Model or in extensions beyond it., Comment: 27 pages, 4 figures

Dynamical Chern-Simons (DCS) gravity, a typical parity-violating gravitational theory, modifies both the generation and propagation of gravitational waves from general relativity (GR). In this work, we derive the gravitational waveform radiated from a binary black hole system with eccentric orbits under the spin-aligned assumption in the DCS theory. Compared with GR, DCS modification enters the second-order post-Newtonian (2PN) approximation, affecting the spin-spin coupling and monopole-quadrupole coupling of binary motion. This modification produces an extra precession rate of periastron. This effect modulates the scalar and gravitational waveform through a quite low frequency. Additionally, the dissipation of conserved quantities results in the secular evolution of the semimajor axis and the eccentricity of binary orbits. Finally, the frequency-domain waveform is given in the post-circular scheme, requiring the initial eccentricity to be $\lesssim0.3$. This ready-to-use template will benefit the signal searches and improve the future constraint on DCS theory., Comment: 33 pages

We present the first analytic results of N$^3$LO QCD corrections to the top-quark decay width. We focus on the dominant leading color contribution, which includes light-quark loops. At NNLO, this dominant contribution accounts for 95% of the total correction. By utilizing the optical theorem, the N$^3$LO corrections are related to the imaginary parts of the four-loop self-energy Feynman diagrams, which are calculated with differential equations. The results are expressed in terms of harmonic polylogarithms, enabling fast and accurate evaluation. The third-order QCD corrections decrease the LO decay width by 0.667%, and the scale uncertainty is reduced by half compared to the NNLO result. The most precise prediction for the top-quark width is now 1.321 GeV for $m_t=172.69$ GeV. Additionally, we obtain the third-order QCD corrections to the dilepton invariant mass spectrum and decay width in the semileptonic $b\to u$ transition., Comment: 11 pages, 2 figures

Accurate electronic structure calculations might be one of the most anticipated applications of quantum computing.The recent landscape of quantum simulations within the Hartree-Fock approximation raises the prospect of substantial theory and hardware developments in this context.Here we propose a general quantum circuit for M{\o}ller-Plesset perturbation theory (MPPT) calculations, which is a popular and powerful post-Hartree-Fock method widly harnessed in solving electronic structure problems. MPPT improves on the Hartree-Fock method by including electron correlation effects wherewith Rayleigh-Schrodinger perturbation theory. Given the Hartree-Fock results, the proposed circuit is designed to estimate the second order energy corrections with MPPT methods. In addition to demonstration of the theoretical scheme, the proposed circuit is further employed to calculate the second order energy correction for the ground state of Helium atom, and the total error rate is around 2.3%. Experiments on IBM 27-qubit quantum computers express the feasibility on near term quantum devices, and the capability to estimate the second order energy correction accurately. In imitation of the classical MPPT, our approach is non-heuristic, guaranteeing that all parameters in the circuit are directly determined by the given Hartree-Fock results. Moreover, the proposed circuit shows a potential quantum speedup comparing to the traditional MPPT calculations. Our work paves the way forward the implementation of more intricate post-Hartree-Fock methods on quantum hardware, enriching the toolkit solving electronic structure problems on quantum computing platforms., Comment: 14 Pages, 4 Figures (in main article)

This paper explores the evolutionary behavior of the Earth-satellite binary system within the framework of the ghost-free parity-violating gravity and the corresponding discussion on the parity-violating effect from the laser-ranged satellites. For this purpose, we start our study with the Parameterized Post-Newtonian (PPN) metric of this gravity theory to study the orbital evolution of the satellites in which the spatial-time sector of the spacetime is modified due to the parity violation. With this modified PPN metric, we calculate the effects of the parity-violating sector of metrics on the time evolution of the orbital elements for an Earth-satellite binary system. We find that among the five orbital elements, the parity violation has no effect on the semi-latus rectum, while the eccentricity and ascending node are affected only in a periodic manner. These three orbital elements are the same as the results of general relativity and are also consistent with the observations of the present experiment. In particular, parity violation produces non-zero corrections to the eccentricity and pericenter, which will accumulate with the evolution of time, indicating that the parity violation of gravity produces observable secular effects. The observational constraint on the parity-violating effect is derived by confronting the theoretical prediction with the observation by the LAGEOS II pericenter advance, giving a constraint on the parity-violating parameter space from the satellite experiments., Comment: 13 pages, no figure

Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world. Despite the advances in dynamic graph neural networks (DGNNs), the rich information and diverse downstream tasks have posed significant difficulties for the practical application of DGNNs in industrial scenarios. To this end, in this paper, we propose to address them by pre-training and present the Contrastive Pre-Training Method for Dynamic Graph Neural Networks (CPDG). CPDG tackles the challenges of pre-training for DGNNs, including generalization capability and long-short term modeling capability, through a flexible structural-temporal subgraph sampler along with structural-temporal contrastive pre-training schemes. Extensive experiments conducted on both large-scale research and industrial dynamic graph datasets show that CPDG outperforms existing methods in dynamic graph pre-training for various downstream tasks under three transfer settings., Comment: 14 pages, 8 figures, accepted by ICDE2024

While network science has become an indispensable tool for studying complex systems, the conventional use of pairwise links often shows limitations in describing high-order interactions properly. Hypergraphs, where each edge can connect more than two nodes, have thus become a new paradigm in network science. Yet, we are still in lack of models linking network growth and hyperedge expansion, both of which are commonly observable in the real world. Here, we propose a generative hypergraph model by employing the preferential attachment mechanism in both nodes and hyperedge formation. The model can produce bi-heterogeneity, exhibiting scale-free distributions in both hyperdegree and hyperedge size. We provide a mean-field treatment that gives the expression of the two scaling exponents, which agree with the numerical simulations. Our model may help to understand the networked systems showing both types of heterogeneity and facilitate the study of complex dynamics thereon., Comment: 11 pages, 6 figures

Recently, ChatGPT, a representative large language model (LLM), has gained considerable attention due to its powerful emergent abilities. Some researchers suggest that LLMs could potentially replace structured knowledge bases like knowledge graphs (KGs) and function as parameterized knowledge bases. However, while LLMs are proficient at learning probabilistic language patterns based on large corpus and engaging in conversations with humans, they, like previous smaller pre-trained language models (PLMs), still have difficulty in recalling facts while generating knowledge-grounded contents. To overcome these limitations, researchers have proposed enhancing data-driven PLMs with knowledge-based KGs to incorporate explicit factual knowledge into PLMs, thus improving their performance to generate texts requiring factual knowledge and providing more informed responses to user queries. This paper reviews the studies on enhancing PLMs with KGs, detailing existing knowledge graph enhanced pre-trained language models (KGPLMs) as well as their applications. Inspired by existing studies on KGPLM, this paper proposes to enhance LLMs with KGs by developing knowledge graph-enhanced large language models (KGLLMs). KGLLM provides a solution to enhance LLMs' factual reasoning ability, opening up new avenues for LLM research.