arXiv subjects

Sibo Wang

Publications and source records attributed to Sibo Wang.

At least 55 records · Page 3Linked to original sources

arXiv2024

Rotating Neutron Stars with the Relativistic Ab Initio Calculations

The equation of state (EOS) of extremely dense matter is crucial for understanding the properties of rotating neutron stars. Starting from the widely used realistic Bonn potentials rooted in a relativistic framework, we derive EOSs by performing the state-of-the-art relativistic Brueckner-Hartree-Fock (RBHF) calculations in the full Dirac space. The self-consistent and simultaneous consideration of both positive- and negative-energy states (NESs) of the Dirac equation allows us to avoid the uncertainties present in calculations where NESs are treated using approximations. To manifest the impact of rotational dynamics, several structural properties of neutron stars across a wide range of rotation frequencies and up to the Keplerian limit are obtained, including the gravitational and baryonic masses, the polar and equatorial radii, and the moments of inertia. Our theoretical predictions align well with the latest astrophysical constraints from the observations on massive neutron stars and joint mass-radius measurements. The maximum mass for rotating configurations can reach up to $2.93M_{\odot}$ for Bonn A potential, while the radius of a $1.4M_\odot$ neutron star for non-rotating case can be extended to around 17 km through the constant baryonic mass sequences. Relations with good universalities between the Keplerian frequency and static mass as well as radius are obtained, from which the radius of the black widow PSR J0952-0607 is predicted to be less than 19.58 km. Furthermore, to understand how rotation deforms the equilibrium shape of a neutron star, the eccentricity is also calculated. The approximate universality between the eccentricity at the Keplerian frequency and the gravitational mass is found.

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arXiv2024

SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection

The smoothing issue in graph learning leads to indistinguishable node representations, posing significant challenges for graph-related tasks. However, our experiments reveal that this problem can uncover underlying properties of node anomaly detection (NAD) that previous research has missed. We introduce Individual Smoothing Patterns (ISP) and Neighborhood Smoothing Patterns (NSP), which indicate that the representations of anomalous nodes are harder to smooth than those of normal ones. In addition, we explore the theoretical implications of these patterns, demonstrating the potential benefits of ISP and NSP for NAD tasks. Motivated by these findings, we propose SmoothGNN, a novel unsupervised NAD framework. First, we design a learning component to explicitly capture ISP for detecting node anomalies. Second, we design a spectral graph neural network to implicitly learn ISP to enhance detection. Third, we design an effective coefficient based on our findings that NSP can serve as coefficients for node representations, aiding in the identification of anomalous nodes. Furthermore, we devise a novel anomaly measure to calculate loss functions and anomalous scores for nodes, reflecting the properties of NAD using ISP and NSP. Extensive experiments on 9 real datasets show that SmoothGNN outperforms the best rival by an average of 14.66% in AUC and 7.28% in Average Precision, with 75x running time speedup, validating the effectiveness and efficiency of our framework.

cs.LG↗

arXiv2024

Examination of the evidence for a proton halo in $^{22}$Al

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.

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arXiv2024

From Perfect to Noisy World Simulation: Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking

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.

cs.CV↗

arXiv2024

Microscopic optical potential from the relativistic Brueckner-Hartree-Fock theory: Proton-nucleus scattering

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.

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arXiv2024

Nuclear mass table in deformed relativistic Hartree-Bogoliubov theory in continuum, II: Even-$Z$ nuclei

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, $α$ 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, $α$ decay energies, rms radii, quadrupole deformations, potential energy curves, neutron density distributions, and neutron mean-field potentials are discussed.

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arXiv2024

Tensor-force effects on nuclear matter in relativistic ab initio theory

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.

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arXiv2024

Towards Deeper Understanding of PPR-based Embedding Approaches: A Topological Perspective

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.

cs.LG↗

arXiv2024

Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness

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.

cs.CV↗

arXiv2024

Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection

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.

cs.LG↗

arXiv2024

Efficient Algorithms for Personalized PageRank Computation: A Survey

Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs. For a pair of nodes $s$ and $t$, the PPR value $π_s(t)$ equals the probability that an $α$-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.

cs.DS↗

arXiv2024

Customizable Perturbation Synthesis for Robust SLAM Benchmarking

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/.

cs.RO↗

arXiv2024

Isospin splitting of the Dirac mass probed by the relativistic Brueckner-Hartree-Fock theory in the full Dirac space

The isospin splitting of the Dirac mass obtained with the relativistic Brueckner-Hartree-Fock (RBHF) theory is thoroughly investigated. From the perspective in the full Dirac space, the long-standing controversy between the momentum-independence approximation (MIA) method and the projection method on the isospin splitting of the Dirac mass in asymmetric nuclear matter (ANM) is analyzed in detail. We find that, the \textit{assumption procedure} of the MIA method, which assumes that the single-particle potentials are momentum independent, is not a sufficient condition that directly leads to the wrong sign of the isospin splitting of the Dirac mass, while the \textit{extraction procedure} of the MIA method, which extracts the single-particle potentials from the single-particle potential energy, leads to the wrong sign. By approximately solving the set of equations involved in the \textit{extraction procedure}, a formal expression of the Dirac mass is obtained. The wrong isospin splitting of the Dirac mass is mainly caused by that the \textit{extraction procedure} forcely assumes the momentum dependence of the single-particle potential energy to be a quadratic form where the strength is solely determined by the constant scalar potential.

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arXiv2023

Low-momentum relativistic nucleon-nucleon potentials I: Nuclear matter

A series of relativistic one-boson-exchange potentials for two-nucleon system, denoted as OBEP$Λ$, is constructed with a momentum cutoff $Λ$ 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$Λ$ ($Λ\leqslant 3$ fm$^{-1}$) demonstrate consistency with the universal behaviors observed in other realistic $NN$ potentials evolved by renormalization group methods. These OBEP$Λ$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$Λ$s signals the necessity of including three-nucleon correlations or genuine three-nucleon forces.

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arXiv2023

A novel design for 100 meter-scale water attenuation length measurement and monitoring

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.

physics.ins-det↗

arXiv2023

Neutron-proton effective mass splitting in neutron-rich matter

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 $0.187α$ with $α$ 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.

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arXiv2023

Subset Sampling and Its Extensions

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 $\mathcal{O}(1+μ_{\mathcal{S}})$ expected \emph{query} time, $\mathcal{O}(n)$ space and $\mathcal{O}(1)$ amortized expected \emph{update}, \emph{insert} and \emph{delete} time, where $μ_{\mathcal{S}}=\sum_{v\in\mathcal{S}}\textbf{p}(v)$. 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 $\mathcal{O}\left((\log^*_B n)/B+(μ_\mathcal{S}/B)\log_{M/B} (n/B)\right)$ 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 $\mathcal{O}(\log n+μ_{\mathcal{S}\cap[a,b]})$ expected \emph{query} time, $\mathcal{O}(n)$ space and $\mathcal{O}(\log n)$ amortized expected \emph{update}, \emph{insert} and \emph{delete} time.

cs.DS↗

arXiv2023

Hadron-quark phase transition in neutron star by combining the relativistic Brueckner-Hartree-Fock theory and Dyson-Schwinger equation approach

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.

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