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In the present study we employ three distinct physically motivated speed of sound bounds in order to construct hybrid models where the high density phase is described by the maximally stiff equation of state. In particular, we consider the bounds related to special relativity, relativistic kinetic theory and conformality. The low density hadronic phase is described by a state-of-the-art microscopic relativistic Brueckner-Hartree-Fock theory. This work aims to access the effect of the different speed of sound constraints on the relevant parameter space of the key parameters of first-order phase transitions by utilising recent astronomical data. This involves a systematic analysis that also includes two distinct schemes for the construction of hybrid models, namely the Maxwell and Gibbs methods. Finally, a relevant discussion is conducted on the possible occurrence of a thermodynamic inconsistency that is related to the stability of the high density phase over hadronic matter at large densities., Comment: 11 pages, 8 Figures

More and more halo nuclei or candidates have been identified or suggested in experiments in recent years. It was declared that the halo structure of $^{22}$Al is revealed by the large isospin asymmetry in $^{22}$Si/$^{22}$O mirror Gamow-Teller transitions [Phys. Rev. Lett. 125, 192503 (2020)]. We highlight that a significant mirror asymmetry already exists between wave functions of the likely unbound nucleus $^{22}$Si and the doubly-magic nucleus $^{22}$O, which largely explains the observed asymmetry in the transitions. Furthermore, these transitions involve only the $1^+$ excited states of the daughter nuclei $^{22}$Al and $^{22}$F. The $1^+$ state of $^{22}$Al cannot be considered a halo state due to its proton-unbound nature. An analysis of the spin-parity suggests that a weakly bound $2s_{1/2}$ valence proton in the ground state of $^{22}$Al is improbable. To investigate the shell structure for the ground state of $^{22}$Al, we employ the state-of-the-art deformed and triaxial relativistic Hartree-Bogoliubov theories in continuum. We find that a small $s$-wave component of $5\%$ appears for the weakly bound valence proton in $^{22}$Al only when triaxial deformation is considered. While the examination of densities and rms radii indicates that this small $s$-wave component is insufficient to form a discernible proton halo in $^{22}$Al, slightly larger $2s_{1/2}$ occupations have been reported in other recent theoretical results. The question of how many low-$\ell$ components are sufficient to form a proton halo in the presence of the Coulomb barrier remains open. Thus, future measurements of reaction or interaction cross sections and momentum distributions of breakup fragments are highly desirable to verify whether $^{22}$Al is a halo nucleus., Comment: Title revised as suggested by Editor, references updated

Embodied agents require robust navigation systems to operate in unstructured environments, making the robustness of Simultaneous Localization and Mapping (SLAM) models critical to embodied agent autonomy. While real-world datasets are invaluable, simulation-based benchmarks offer a scalable approach for robustness evaluations. However, the creation of a challenging and controllable noisy world with diverse perturbations remains under-explored. To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations. The pipeline comprises a comprehensive taxonomy of sensor and motion perturbations for embodied multi-modal (specifically RGB-D) sensing, categorized by their sources and propagation order, allowing for procedural composition. We also provide a toolbox for synthesizing these perturbations, enabling the transformation of clean environments into challenging noisy simulations. Utilizing the pipeline, we instantiate the large-scale Noisy-Replica benchmark, which includes diverse perturbation types, to evaluate the risk tolerance of existing advanced RGB-D SLAM models. Our extensive analysis uncovers the susceptibilities of both neural (NeRF and Gaussian Splatting -based) and non-neural SLAM models to disturbances, despite their demonstrated accuracy in standard benchmarks. Our code is publicly available at https://github.com/Xiaohao-Xu/SLAM-under-Perturbation., Comment: 50 pages. arXiv admin note: substantial text overlap with arXiv:2402.08125

The emergence of Large Vision-Language Models (LVLMs) marks significant strides towards achieving general artificial intelligence. However, these advancements are tempered by the outputs that often reflect biases, a concern not yet extensively investigated. Existing benchmarks are not sufficiently comprehensive in evaluating biases due to their limited data scale, single questioning format and narrow sources of bias. To address this problem, we introduce VLBiasBench, a benchmark aimed at evaluating biases in LVLMs comprehensively. In VLBiasBench, we construct a dataset encompassing nine distinct categories of social biases, including age, disability status, gender, nationality, physical appearance, race, religion, profession, social economic status and two intersectional bias categories (race x gender, and race x social economic status). To create a large-scale dataset, we use Stable Diffusion XL model to generate 46,848 high-quality images, which are combined with different questions to form 128,342 samples. These questions are categorized into open and close ended types, fully considering the sources of bias and comprehensively evaluating the biases of LVLM from multiple perspectives. We subsequently conduct extensive evaluations on 15 open-source models as well as one advanced closed-source model, providing some new insights into the biases revealing from these models. Our benchmark is available at https://github.com/Xiangkui-Cao/VLBiasBench.

Node embedding learns low-dimensional vectors for nodes in the graph. Recent state-of-the-art embedding approaches take Personalized PageRank (PPR) as the proximity measure and factorize the PPR matrix or its adaptation to generate embeddings. However, little previous work analyzes what information is encoded by these approaches, and how the information correlates with their superb performance in downstream tasks. In this work, we first show that state-of-the-art embedding approaches that factorize a PPR-related matrix can be unified into a closed-form framework. Then, we study whether the embeddings generated by this strategy can be inverted to better recover the graph topology information than random-walk based embeddings. To achieve this, we propose two methods for recovering graph topology via PPR-based embeddings, including the analytical method and the optimization method. Extensive experimental results demonstrate that the embeddings generated by factorizing a PPR-related matrix maintain more topological information, such as common edges and community structures, than that generated by random walks, paving a new way to systematically comprehend why PPR-based node embedding approaches outperform random walk-based alternatives in various downstream tasks. To the best of our knowledge, this is the first work that focuses on the interpretability of PPR-based node embedding approaches.

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $ n \rightarrow { inv} $ and $ nn \rightarrow { inv} $. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$., Comment: 28 pages, 7 figures, 4 tables

The smoothing issue leads to indistinguishable node representations, which poses a significant challenge in the field of graph learning. However, this issue also presents an opportunity to reveal underlying properties behind different types of nodes, which have been overlooked in previous studies. Through empirical and theoretical analysis of real-world node anomaly detection (NAD) datasets, we observe that anomalous and normal nodes show different patterns in the smoothing process, which can be leveraged to enhance NAD tasks. Motivated by these findings, in this paper, we propose a novel unsupervised NAD framework. Specifically, according to our theoretical analysis, we design a Smoothing Learning Component. Subsequently, we introduce a Smoothing-aware Spectral Graph Neural Network, which establishes the connection between the spectral space of graphs and the smoothing process. Additionally, we demonstrate that the Dirichlet Energy, which reflects the smoothness of a graph, can serve as coefficients for node representations across different dimensions of the spectral space. Building upon these observations and analyses, we devise a novel anomaly measure for the NAD task. Extensive experiments on 9 real-world datasets show that SmoothGNN outperforms the best rival by an average of 14.66% in AUC and 7.28% in Precision, with 75x running time speed-up, which validates the effectiveness and efficiency of our framework.

The proliferation of rumors on social media platforms during significant events, such as the US elections and the COVID-19 pandemic, has a profound impact on social stability and public health. Existing approaches for rumor detection primarily rely on propagation graphs to enhance model effectiveness. However, the presence of noisy and irrelevant structures during the propagation process limits the efficacy of these approaches. To tackle this issue, techniques such as weight adjustment and data augmentation have been proposed. However, these techniques heavily depend on rich original propagation structures, thus hindering performance when dealing with rumors that lack sufficient propagation information in the early propagation stages. In this paper, we propose Key Propagation Graph Generator (KPG), a novel reinforcement learning-based rumor detection framework that generates contextually coherent and informative propagation patterns for events with insufficient topology information, while also identifies indicative substructures for events with redundant and noisy propagation structures. KPG consists of two key components: the Candidate Response Generator (CRG) and the Ending Node Selector (ENS). CRG learns the latent distribution from refined propagation patterns, filtering out noise and generating new candidates for ENS. Simultaneously, ENS identifies the most influential substructures within propagation graphs and generates training data for CRG. Moreover, we introduce an end-to-end framework that utilizes rewards to guide the entire training process via a pre-trained graph neural network. Extensive experiments conducted on four datasets demonstrate the superiority of our KPG compared to the state-of-the-art approaches.

Within the relativistic Brueckner-Hartree-Fock theory in the full Dirac space, the tensor-force effects on infinite nuclear matter are elucidated by subtracting the matrix elements of tensor forces from the realistic nucleon-nucleon interaction. The tensor-force effects for the binding energy per particle of symmetric nuclear matter (SNM) as well as the symmetry energy are attractive and are more pronounced around the empirical saturation density, while the tensor forces have little impact on the pure neutron matter. By tuning the tensor-force strength, an infinite (negative) scattering length in the spin-triplet channel is found. This locates the dilute SNM with only the $^3S_1$-$^3D_1$ channel interaction at the unitary limit. Its ground-state energy is found proportional to the energy of a free Fermi gas with a scaling factor 0.38, revealing good universal properties. This work paves the way to study the tensor-force effects in neutron stars as well as finite nuclei from realistic nucleon-nucleon interactions, highlights the role of the tensor force on the deviation of the nuclear physics to the unitary limit, and provides valuable reference for studies of the four-component unitary Fermi gas., Comment: 5 pages, 2 figures, discussion on four-component unitary Fermi gas is updated, accepted by Science Bulletin

Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs. For a pair of nodes $s$ and $t$, the PPR value $\pi_s(t)$ equals the probability that an $\alpha$-discounted random walk from $s$ terminates at $t$ and reflects the importance between $s$ and $t$ in a bidirectional way. As a generalization of Google's celebrated PageRank centrality, PPR has been extensively studied and has found multifaceted applications in many fields, such as network analysis, graph mining, and graph machine learning. Despite numerous studies devoted to PPR over the decades, efficient computation of PPR remains a challenging problem, and there is a dearth of systematic summaries and comparisons of existing algorithms. In this paper, we recap several frequently used techniques for PPR computation and conduct a comprehensive survey of various recent PPR algorithms from an algorithmic perspective. We classify these approaches based on the types of queries they address and review their methodologies and contributions. We also discuss some representative algorithms for computing PPR on dynamic graphs and in parallel or distributed environments., Comment: 20 pages, "accepted version" of an article accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE) for publication

A relativistic microscopic optical model potential for nucleon-nucleus scattering is developed based on the \emph{ab initio} relativistic Brueckner-Hartree-Fock (RBHF) theory with the improved local density approximation, which is abbreviated as the RBOM potential. Both real and imaginary parts of the single-particle potentials in symmetric and asymmetric nuclear matter at various densities are determined uniquely in the full Dirac space. The density distributions of the target nuclei are calculated by the covariant energy density functional theory with the density functional PC-PK1. The central and spin-orbit terms of the optical potentials are quantitatively consistent with the relativistic phenomenological optical potentials. The performance of the RBOM potential is evaluated by considering proton scattering with incident energy $E\leq 200$ MeV on five target nuclei, $\prescript{208}{}{\text{Pb}}$, $\prescript{120}{}{\text{Sn}}$, $\prescript{90}{}{\text{Zr}}$, $\prescript{48}{}{\text{Ca}}$, and $\prescript{40}{}{\text{Ca}}$. Scattering observables including the elastic scattering angular distributions, analyzing powers, spin rotation functions, and reaction cross sections are analyzed. Theoretical predictions show good agreements with the experimental data and the results derived from phenomenological optical potentials. We anticipate that the RBOM potential can provide reference for other phenomenological and microscopic optical model potentials, as well as reliable descriptions for nucleon scattering on exotic nuclei in the era of rare-isotope beams., Comment: 36 pages, 20 figures, published as Phys. Rev. C 109, 064603 (2024), Editor's Suggestion

Robustness is a crucial factor for the successful deployment of robots in unstructured environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM). Simulation-based benchmarks have emerged as a highly scalable approach for robustness evaluation compared to real-world data collection. However, crafting a challenging and controllable noisy world with diverse perturbations remains relatively under-explored. To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations. This pipeline incorporates customizable hardware setups, software components, and perturbed environments. In particular, we introduce comprehensive perturbation taxonomy along with a perturbation composition toolbox, allowing the transformation of clean simulations into challenging noisy environments. Utilizing the pipeline, we instantiate the Robust-SLAM benchmark, which includes diverse perturbation types, to evaluate the risk tolerance of existing advanced multi-modal SLAM models. Our extensive analysis uncovers the susceptibilities of existing SLAM models to real-world disturbance, despite their demonstrated accuracy in standard benchmarks. Our perturbation synthesis toolbox, SLAM robustness evaluation pipeline, and Robust-SLAM benchmark will be made publicly available at https://github.com/Xiaohao-Xu/SLAM-under-Perturbation/., Comment: 40 pages

The mass table in the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with the PC-PK1 density functional has been established for even-$Z$ nuclei with $8\le Z\le120$, extended from the previous work for even-even nuclei [Zhang $\it{et.~al.}$ (DRHBc Mass Table Collaboration), At. Data Nucl. Data Tables 144, 101488 (2022)]. The calculated binding energies, two-nucleon and one-neutron separation energies, root-mean-square (rms) radii of neutron, proton, matter, and charge distributions, quadrupole deformations, and neutron and proton Fermi surfaces are tabulated and compared with available experimental data. A total of 4829 even-$Z$ nuclei are predicted to be bound, with an rms deviation of 1.477 MeV from the 1244 mass data. Good agreement with the available experimental odd-even mass differences, $\alpha$ decay energies, and charge radii is also achieved. The description accuracy for nuclear masses and nucleon separation energies as well as the prediction for drip lines is compared with the results obtained from other relativistic and nonrelativistic density functional. The comparison shows that the DRHBc theory with PC-PK1 provides an excellent microscopic description for the masses of even-$Z$ nuclei. The systematics of the nucleon separation energies, odd-even mass differences, pairing energies, two-nucleon gaps, $\alpha$ decay energies, rms radii, quadrupole deformations, potential energy curves, neutron density distributions, and neutron mean-field potentials are discussed., Comment: 394 pages, 17 figures, 2 tables, published in Atomic Data and Nuclear Data Tables, data file in the TXT form is available for download under "Ancillary files"

Large-scale pre-trained vision-language models like CLIP have demonstrated impressive performance across various tasks, and exhibit remarkable zero-shot generalization capability, while they are also vulnerable to imperceptible adversarial examples. Existing works typically employ adversarial training (fine-tuning) as a defense method against adversarial examples. However, direct application to the CLIP model may result in overfitting, compromising the model's capacity for generalization. In this paper, we propose Pre-trained Model Guided Adversarial Fine-Tuning (PMG-AFT) method, which leverages supervision from the original pre-trained model by carefully designing an auxiliary branch, to enhance the model's zero-shot adversarial robustness. Specifically, PMG-AFT minimizes the distance between the features of adversarial examples in the target model and those in the pre-trained model, aiming to preserve the generalization features already captured by the pre-trained model. Extensive Experiments on 15 zero-shot datasets demonstrate that PMG-AFT significantly outperforms the state-of-the-art method, improving the top-1 robust accuracy by an average of 4.99%. Furthermore, our approach consistently improves clean accuracy by an average of 8.72%. Our code is available at https://github.com/serendipity1122/Pre-trained-Model-Guided-Fine-Tuning-for-Zero-Shot-Adversarial-Robustness., Comment: Accepted by CVPR 2024

A series of relativistic one-boson-exchange potentials for two-nucleon system, denoted as OBEP$\Lambda$, is constructed with a momentum cutoff $\Lambda$ ranging from $\infty$ to 2 fm$^{-1}$. These potentials are developed by simultaneous fitting to nucleon-nucleon ($NN$) scattering phase shifts, low-energy scattering length, effective range, and the binding energy of the deuteron. The momentum-space matrix elements of the low-momentum OBEP$\Lambda$ ($\Lambda\leqslant 3$ fm$^{-1}$) demonstrate consistency with the universal behaviors observed in other realistic $NN$ potentials evolved by renormalization group methods. These OBEP$\Lambda$s are applied to calculate the equation of state of symmetric nuclear matter (SNM) within either the nonrelativistic (NR) Brueckner-Hartree-Fock (BHF) or relativistic Brueckner-Hartree-Fock (RBHF) frameworks. The results show that the saturation properties of SNM are reproduced qualitatively from the RBHF calculation, but not from the NR-BHF calculation. This study highlights the relativistic mechanism in explaining the saturation properties of nuclear matter. The remaining discrepancy in reproducing empirical saturation properties in the RBHF calculation using the OBEP$\Lambda$s signals the necessity of including three-nucleon correlations or genuine three-nucleon forces.

Water Cherenov detector is a vital part in most of neutrino or cosmic ray research. As detectors grow in size, the water attenuation length (WAL) becomes increasingly essential for detector performance. It is essential to measure or monitor the WAL. While many experiments have measured WAL in the lab or detector, only the Super-Kamiokande experiment has achieved values exceeding 50 meters in the detector with a moving light source. However, it is impractical for many experiments to place a moving light source inside the detector, necessitating an alternative method for investigating long WAL. A novel system has been proposed to address the challenge of investigating long WAL. This system focuses on ample water Cherenkov detectors and features a fixed light source and photomultiplier tubes (PMTs) at varying distances, eliminating the need for moving parts. The static setup demands high precision for accurate measurement of long WAL. Each component, including LED, diffuse ball, PMTs, and fibers, is introduced to explain uncertainty control. Based on lab tests, the system's uncertainty has been controlled within 5\%. Additionally, camera technology is also used during the evaluation of the system uncertainty, which has the potential to replace PMTs in the future for this measurement. Monte Carlo simulations have shown that the system can achieve a 5\% measurement uncertainty at WAL of 80 meters and 8\% at WAL of 100 meters. This system can be used in experiments with large Cherenkov detectors such as JUNO water veto and Hyper-K.

Graph-level anomaly detection has gained significant attention as it finds applications in various domains, such as cancer diagnosis and enzyme prediction. However, existing methods fail to capture the spectral properties of graph anomalies, resulting in unexplainable framework design and unsatisfying performance. In this paper, we re-investigate the spectral differences between anomalous and normal graphs. Our main observation shows a significant disparity in the accumulated spectral energy between these two classes. Moreover, we prove that the accumulated spectral energy of the graph signal can be represented by its Rayleigh Quotient, indicating that the Rayleigh Quotient is a driving factor behind the anomalous properties of graphs. Motivated by this, we propose Rayleigh Quotient Graph Neural Network (RQGNN), the first spectral GNN that explores the inherent spectral features of anomalous graphs for graph-level anomaly detection. Specifically, we introduce a novel framework with two components: the Rayleigh Quotient learning component (RQL) and Chebyshev Wavelet GNN with RQ-pooling (CWGNN-RQ). RQL explicitly captures the Rayleigh Quotient of graphs and CWGNN-RQ implicitly explores the spectral space of graphs. Extensive experiments on 10 real-world datasets show that RQGNN outperforms the best rival by 6.74% in Macro-F1 score and 1.44% in AUC, demonstrating the effectiveness of our framework. Our code is available at https://github.com/xydong127/RQGNN.

This paper studies the \emph{subset sampling} problem. The input is a set $\mathcal{S}$ of $n$ records together with a function $\textbf{p}$ that assigns each record $v\in\mathcal{S}$ a probability $\textbf{p}(v)$. A query returns a random subset $X$ of $\mathcal{S}$, where each record $v\in\mathcal{S}$ is sampled into $X$ independently with probability $\textbf{p}(v)$. The goal is to store $\mathcal{S}$ in a data structure to answer queries efficiently. If $\mathcal{S}$ fits in memory, the problem is interesting when $\mathcal{S}$ is dynamic. We develop a dynamic data structure with $\mathcal{O}(1+\mu_{\mathcal{S}})$ expected \emph{query} time, $\mathcal{O}(n)$ space and $\mathcal{O}(1)$ amortized expected \emph{update}, \emph{insert} and \emph{delete} time, where $\mu_{\mathcal{S}}=\sum_{v\in\mathcal{S}}\textbf{p}(v)$. The query time and space are optimal. If $\mathcal{S}$ does not fit in memory, the problem is difficult even if $\mathcal{S}$ is static. Under this scenario, we present an I/O-efficient algorithm that answers a \emph{query} in $\mathcal{O}\left((\log^*_B n)/B+(\mu_\mathcal{S}/B)\log_{M/B} (n/B)\right)$ amortized expected I/Os using $\mathcal{O}(n/B)$ space, where $M$ is the memory size, $B$ is the block size and $\log^*_B n$ is the number of iterative $\log_2(.)$ operations we need to perform on $n$ before going below $B$. In addition, when each record is associated with a real-valued key, we extend the \emph{subset sampling} problem to the \emph{range subset sampling} problem, in which we require that the keys of the sampled records fall within a specified input range $[a,b]$. For this extension, we provide a solution under the dynamic setting, with $\mathcal{O}(\log n+\mu_{\mathcal{S}\cap[a,b]})$ expected \emph{query} time, $\mathcal{O}(n)$ space and $\mathcal{O}(\log n)$ amortized expected \emph{update}, \emph{insert} and \emph{delete} time., Comment: 17 pages

Nucleon effective masses in neutron-rich matter are studied with the relativistic Brueckner-Hartree-Fock (RBHF) theory in the full Dirac space. The neutron and proton effective masses for symmetric nuclear matter are 0.80 times rest mass, which agrees well with the empirical values. In neutron-rich matter, the effective mass of the neutron is found larger than that of the proton, and the neutron-proton effective mass splittings at the empirical saturation density are predicted as $0.187\alpha$ with $\alpha$ being the isospin asymmetry parameter. The result is compared to other ab initio calculations and is consistent with the constraints from the nuclear reaction and structure measurements, such as the nucleon-nucleus scattering, the giant resonances of $^{208}$Pb, and the Hugenholtz-Van Hove theorem with systematics of nuclear symmetry energy and its slope. The predictions of the neutron-proton effective mass splitting from the RBHF theory in the full Dirac space might be helpful to constrain the isovector parameters in phenomenological density functionals., Comment: 14 pages, 4 figures, the same as the online version

Accurate prediction of the force required to puncture a soft material is critical in many fields like medical technology, food processing, and manufacturing. However, such a prediction strongly depends on our understanding of the complex nonlinear behavior of the material subject to deep indentation and complex failure mechanisms. Only recently we developed theories capable of correlating puncture force with material properties and needle geometry. However, such models are based on simplifications that seldom limit their applicability to real cases. One common assumption is the incompressibility of the cut material, albeit no material is truly incompressible. In this paper we propose a simple model that accounts for linearly elastic compressibility, and its interplay with toughness, stiffness, and elastic strain-stiffening. Confirming previous theories and experiments, materials having high-toughness and low-modulus exhibit the highest puncture resistance at a given needle radius. Surprisingly, in these conditions, we observe that incompressible materials exhibit the lowest puncture resistance, where volumetric compressibility can create an additional (strain) energy barrier to puncture. Our model provides a valuable tool to assess the puncture resistance of soft compressible materials and suggests new design strategies for sharp needles and puncture-resistant materials.

Starting from the relativistic Brueckner-Hartree-Fock theory for nuclear matter and the Dyson-Schwinger equation approach for quark matter, the possible hadron-quark phase transition in the interior of a neutron star is explored. The first-order phase transition and crossover are studied by performing the Maxwell construction and three-window construction respectively. The mass-radius relation and the tidal deformability of the hybrid star are calculated and compared to the joint mass-radius observation of a neutron star and the constraints from gravitational wave detection. For the Maxwell construction, no stable quark core is found in the interior of a neutron star. For the three-window construction, the parameters of the smooth interpolation function are chosen in such a way to keep the thermodynamic stability and lead to a moderate crossover density region. To support a two-solar-mass neutron star under the three-window construction, the effective width of medium screening effects in quark matter should be around $0.35$ GeV., Comment: 27 pages, 12 figures, 2 tables, published on Phys.Rev.D 107, 103009 (2023)

Relativistic Brueckner-Hartree-Fock (RBHF) theory in the full Dirac space allows one to determine uniquely the momentum dependence of scalar and vector components of the single-particle potentials. In order to extend this new method from nuclear matter to finite nuclei, as a first step, properties of $^{208}$Pb are explored by using the microscopic equation of state for asymmetric nuclear matter and a liquid droplet model. The neutron and proton density distributions, the binding energies, the neutron and proton radii, and the neutron skin thickness in $^{208}$Pb are calculated. In order to further compare the charge densities predicted from the RBHF theory in the full Dirac space with the experimental charge densities, the differential cross sections and the electric charge form factors in the elastic electron-nucleus scattering are obtained by using the phase-shift analysis method. The results from the RBHF theory are in good agreement with the experimental data. In addition, the uncertainty arising from variations of the surface term parameter $f_0$ in the liquid droplet model is also discussed.

{\em Personalized PageRank (PPR)} stands as a fundamental proximity measure in graph mining. Since computing an exact SSPPR query answer is prohibitive, most existing solutions turn to approximate queries with guarantees. The state-of-the-art solutions for approximate SSPPR queries are index-based and mainly focus on static graphs, while real-world graphs are usually dynamically changing. However, existing index-update schemes can not achieve a sub-linear update time. Motivated by this, we present an efficient indexing scheme to maintain indexed random walks in expected $O(1)$ time after each graph update. To reduce the space consumption, we further propose a new sampling scheme to remove the auxiliary data structure for vertices while still supporting $O(1)$ index update cost on evolving graphs. Extensive experiments show that our update scheme achieves orders of magnitude speed-up on update performance over existing index-based dynamic schemes without sacrificing the query efficiency.

In modern graph analytics, the shortest path is a fundamental concept. Numerous \rrev{recent works} concentrate mostly on the distance of these shortest paths. Nevertheless, in the era of betweenness analysis, the counting of the shortest path between $s$ and $t$ is equally crucial. \rrev{It} is \rev{also} an important issue in the area of graph databases. In recent years, several studies have been conducted in an effort to tackle such issues. Nonetheless, the present technique faces a considerable barrier to parallel due to the dependencies in the index construction stage, hence limiting its application possibilities and wasting the potential hardware performance. To address this problem, we provide a parallel shortest path counting method that could avoid these dependencies and obtain approximately linear index time speedup as the number of threads increases. Our empirical evaluations verify the efficiency and effectiveness.

The momentum and isospin dependence of the single-particle potential for the in-medium nucleon are the key quantities in the Relativistic Brueckner-Hartree-Fock (RBHF) theory. It depends on how to extract the scalar and the vector components of the single-particle potential inside nuclear matter. In contrast to the RBHF calculations in the Dirac space with the positive-energy states (PESs) only, the single-particle potential can be determined in a unique way by the RBHF theory together with the negative-energy states (NESs), i.e., the RBHF theory in the full Dirac space. The saturation properties of symmetric and asymmetric nuclear matter in the full Dirac space are systematically investigated based on the realistic Bonn nucleon-nucleon potentials. In order to further specify the importance of the calculations in the full Dirac space, the neutron star properties are investigated. The direct URCA process in neutron star cooling will happen at density $\rho_{\rm{DURCA}}=0.43,~0.48,~0.52$ fm$^{-3}$ with the proton fractions $Y_{p,\rm{DURCA}}=0.13$. The radii of a $1.4M_\odot$ neutron star are predicated as $R_{1.4M_\odot}=11.97,~12.13,~12.27$ km, and their tidal deformabilities are $\Lambda_{1.4M_\odot}=376,~405,~433$ for potential Bonn A, B, C. Comparing with the results obtained in the Dirac space with PESs only, full-Dirac-space RBHF calculation predicts the softest symmetry energy which would be more favored by the gravitational waves (GW) detection from GW170817. Furthermore, the results from full-Dirac-space RBHF theory are consistent with the recent astronomical observations of massive neutron stars and simultaneous mass-radius measurement.

Community search is a problem that seeks cohesive and connected subgraphs in a graph that satisfy certain topology constraints, e.g., degree constraints. The majority of existing works focus exclusively on the topology and ignore the nodes' influence in the communities. To tackle this deficiency, influential community search is further proposed to include the node's influence. Each node has a weight, namely influence value, in the influential community search problem to represent its network influence. The influence value of a community is produced by an aggregated function, e.g., max, min, avg, and sum, over the influence values of the nodes in the same community. The objective of the influential community search problem is to locate the top-r communities with the highest influence values while satisfying the topology constraints. Existing studies on influential community search have several limitations: (i) they focus exclusively on simple aggregation functions such as min, which may fall short of certain requirements in many real-world scenarios, and (ii) they impose no limitation on the size of the community, whereas most real-world scenarios do. This motivates us to conduct a new study to fill this gap. We consider the problem of identifying the top-r influential communities with/without size constraints while using more complicated aggregation functions such as sum or avg. We give a theoretical analysis demonstrating the hardness of the problems and propose efficient and effective heuristic solutions for our topr influential community search problems. Extensive experiments on real large graphs demonstrate that our proposed solution is significantly more efficient than baseline solutions.

Starting from the relativistic realistic nucleon-nucleon ($NN$) interactions, a newly developed relativistic \textit{ab initio} method, i.e., the relativistic Brueckner-Hartree-Fock (RBHF) theory in the full Dirac space is employed to study the neutron star properties. First, the one-to-one correspondence relation for gravitational redshift and mass is established and used to infer the mass of isolated neutron stars combining the gravitational redshift measurements. Next, the ratio of the moment of inertia $I$ to $MR^2$ as a function of the compactness $M/R$ is obtained, which is consistent with the universal relations in the literature. The moment of inertia for $1.338M_\odot$ pulsar PSR J0737-3039A $I_{1.338M_\odot}$ is predicted to be 1.356$\times10^{45}$, 1.381$\times10^{45}$, and $1.407\times10^{45}\ \mathrm{g~cm^2}$ by the RBHF theory in the full Dirac space with $NN$ interactions Bonn A, B, and C, respectively. Finally, the quadrupole moment of neutron star is calculated under the slow-rotation and small-tidal-deformation approximation. The equation of states constructed by the RBHF theory in the full Dirac space, together with those by the projection method and momentum-independence approximation, conform to universal $I$-Love-$Q$ relations as well. By combing the tidal deformability from GW170817 and the universal relations from relativistic \textit{ab initio} methods, the moment of inertia of neutron star with 1.4 solar mass is also deduced as $I_{1.4M_\odot}=1.22^{+0.40}_{-0.25}\times 10^{45}\mathrm{g\ cm^2}$., Comment: 21 pages, 5 figures

Graph Neural Networks (GNNs) have been widely used for modeling graph-structured data. With the development of numerous GNN variants, recent years have witnessed groundbreaking results in improving the scalability of GNNs to work on static graphs with millions of nodes. However, how to instantly represent continuous changes of large-scale dynamic graphs with GNNs is still an open problem. Existing dynamic GNNs focus on modeling the periodic evolution of graphs, often on a snapshot basis. Such methods suffer from two drawbacks: first, there is a substantial delay for the changes in the graph to be reflected in the graph representations, resulting in losses on the model's accuracy; second, repeatedly calculating the representation matrix on the entire graph in each snapshot is predominantly time-consuming and severely limits the scalability. In this paper, we propose Instant Graph Neural Network (InstantGNN), an incremental computation approach for the graph representation matrix of dynamic graphs. Set to work with dynamic graphs with the edge-arrival model, our method avoids time-consuming, repetitive computations and allows instant updates on the representation and instant predictions. Graphs with dynamic structures and dynamic attributes are both supported. The upper bounds of time complexity of those updates are also provided. Furthermore, our method provides an adaptive training strategy, which guides the model to retrain at moments when it can make the greatest performance gains. We conduct extensive experiments on several real-world and synthetic datasets. Empirical results demonstrate that our model achieves state-of-the-art accuracy while having orders-of-magnitude higher efficiency than existing methods.

The long-standing controversy about the isospin dependence of the effective Dirac mass in ab initio calculations of asymmetric nuclear matter is clarified by solving the relativistic Brueckner-Hartree-Fock equations in the full Dirac space. The symmetry energy and its slope parameter at the saturation density are $E_{\text{sym}}(\rho_0)=33.1$ MeV and $L=65.2$ MeV, in agreement with empirical and experimental values. Further applications predict the neutron star radius $R_{1.4M_\odot}\approx 12$ km and the maximum mass of a neutron star $M_{\text{max}}\leq 2.4M_\odot$., Comment: 6 pages, 3 figures