arXiv subjects

Jun Xu

Publications and source records attributed to Jun Xu.

At least 289 records · Page 16Linked to original sources

arXiv2023

Revisiting angular momentum conservation in transport simulations of intermediate-energy heavy-ion collisions

Based on the well-calibrated IBUU transport model, we have studied the dynamical effect of incorporating rigorous angular momentum conservation in each collision of particles with homework setups. The constraint of the rigorous angular momentum conservation requires in-plane collisions and side jumps of particles after their collision. Since the option is not unique, we have compared two typical prescriptions with the original one. While the results depend quantitatively on the choice of the prescription, we found that the angular momentum conservation generally reduces local density fluctuations and thus the collision rate, and may have some influence on the density evolution, the collective flow, and even the pion production in transport simulations of intermediate-energy heavy-ion collisions.

nucl-th↗

arXiv2023

Bayesian inference of nucleus resonance and neutron skin

In this proceeding, we have presented some highlight results on the constraints of the nuclear matter equation of state (EOS) from the data of nucleus resonance and neutron-skin thickness using the Bayesian approach based on the Skyrme-Hartree-Fock model and its extension. Typically, we have discussed the anti-correlation and positive correlation between the slope parameter and the value of the symmetry energy at the saturation density under the constraint of, respectively, the neutron-skin thickness and the isovector giant dipole resonance. We have shown that the Bayesian analysis can help to find a compromise for the ``PREXII puzzle'' and the ``soft Tin puzzle". We have further illustrated the possible modifications on the constraints of lower-order EOS parameters as well as the relevant correlation when higher-order EOS parameters are incorporated as independent variables. For a given model and parameter space, the Bayesian approach serves as a good analysis tool suitable for multi-messengers versus multi-variables, and is helpful for constraining quantitatively the model parameters as well as their correlations.

nucl-th↗

arXiv2023

Multiwavelength Analysis of a Nearby Heavily Obscured AGN in NGC 449

We presented the multiwavelength analysis of a heavily obscured active galactic nucleus (AGN) in NGC 449. We first constructed a broadband X-ray spectrum using the latest NuSTAR and XMM-Newton data. Its column density ($\simeq 10^{24} \rm{cm}^{-2}$) and photon index ($Γ\simeq 2.4$) were reliably obtained by analyzing the broadband X-ray spectrum. However, the scattering fraction and the intrinsic X-ray luminosity could not be well constrained. Combined with the information obtained from the mid-infrared (mid-IR) spectrum and spectral energy distribution (SED) fitting, we derived its intrinsic X-ray luminosity ($\simeq 8.54\times 10^{42} \ \rm{erg\ s}^{-1}$) and scattering fraction ($f_{\rm{scat}}\simeq 0.26\%$). In addition, we also derived the following results: (1). The mass accretion rate of central AGN is about $2.54 \times 10^{-2} \rm{M}_\odot\ \rm{yr}^{-1}$, and the Eddington ratio is $8.39\times 10^{-2}$; (2). The torus of this AGN has a high gas-to-dust ratio ($N_{\rm H}/A_{\rm V}=8.40\times 10^{22}\ \rm{cm}^{-2}\ \rm{mag}^{-1}$); (3). The host galaxy and the central AGN are both in the early stage of co-evolution.

astro-ph.GA↗

arXiv2022

Peering into the Milky Way by FAST: III. Magnetic fields in the Galactic halo and farther spiral arms revealed by the Faraday effect of faint pulsars

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) is the most sensitive radio telescope for pulsar observations. We make polarimetric measurements of a large number of faint and distant pulsars using the FAST. We present the new measurements of Faraday rotation for 134 faint pulsars in the Galactic halo. Significant improvements are also made for some basic pulsar parameters for 15 of them. We analyse the newly determined rotation measures (RMs) for the Galactic magnetic fields by using these 134 halo pulsars, together with previously available RMs for pulsars and extragalactic radio sources and also the newly determined RMs for another 311 faint pulsars which are either newly discovered in the project of the Galactic Plane Pulsar Snapshot (GPPS) survey or previously known pulsars without RMs. The RM tomographic analysis in the first Galactic quadrant gives roughly the same field strength of around 2~$μ$G for the large-scale toroidal halo magnetic fields. The scale height of the halo magnetic fields is found to be at least 2.7$\pm$0.3~kpc. The RM differentiation of a large number of pulsars in the Galactic disk in the Galactic longitude range of $26^{\circ}<l<90^{\circ}$ gives evidence for the clockwise magnetic fields (viewed from the north Galactic pole) in two interarm regions inside the Scutum arm and between the Scutum and Sagittarius arm, and the clockwise fields in the Local-Perseus interarm region and field reversals in the Perseus arm and beyond.

astro-ph.GA↗

arXiv2022

Data-driven approximation of control invariant set for linear system based on convex piecewise linear fitting

Control invariant set is critical for guaranteeing safe control and the problem of computing control invariant set for linear discrete-time system is revisited in this paper by using a data-driven approach. Specifically, sample points on convergent trajectories of linear MPC are recorded, of which the convex hull formulates a control invariant set for the linear system. To approximate the convex hull of multiple sample points, a convex piecewise linear (PWL) fitting framework has been proposed, which yields a polyhedral approximation with predefined complexity. A descent algorithm for the convex PWL fitting problem is also developed, which is guaranteed to converge to a local optimum. The proposed strategy is flexible in computing the control invariant set in high dimension with a predefined complexity. Simulation results show that the proposed data-driven approximation can compute the approximated control invariant set with high accuracy and relatively low computational cost.

math.OC↗

arXiv2022

PLATO-K: Internal and External Knowledge Enhanced Dialogue Generation

Recently, the practical deployment of open-domain dialogue systems has been plagued by the knowledge issue of information deficiency and factual inaccuracy. To this end, we introduce PLATO-K based on two-stage dialogic learning to strengthen internal knowledge memorization and external knowledge exploitation. In the first stage, PLATO-K learns through massive dialogue corpora and memorizes essential knowledge into model parameters. In the second stage, PLATO-K mimics human beings to search for external information and to leverage the knowledge in response generation. Extensive experiments reveal that the knowledge issue is alleviated significantly in PLATO-K with such comprehensive internal and external knowledge enhancement. Compared to the existing state-of-the-art Chinese dialogue model, the overall engagingness of PLATO-K is improved remarkably by 36.2% and 49.2% on chit-chat and knowledge-intensive conversations.

cs.CL↗

arXiv2022

Benchmarking Quantum(-inspired) Annealing Hardware on Practical Use Cases

Quantum(-inspired) annealers show promise in solving combinatorial optimisation problems in practice. There has been extensive researches demonstrating the utility of D-Wave quantum annealer and quantum-inspired annealer, i.e., Fujitsu Digital Annealer on various applications, but few works are comparing these platforms. In this paper, we benchmark quantum(-inspired) annealers with three combinatorial optimisation problems ranging from generic scientific problems to complex problems in practical use. In the case where the problem size goes beyond the capacity of a quantum(-inspired) computer, we evaluate them in the context of decomposition. Experiments suggest that both annealers are effective on problems with small size and simple settings, but lose their utility when facing problems in practical size and settings. Decomposition methods extend the scalability of annealers, but they are still far away from practical use. Based on the experiments and comparison, we discuss the advantages and limitations of quantum(-inspired) annealers, as well as the research directions that may improve the utility and scalability of the these emerging computing technologies.

quant-ph↗

arXiv2022

Probing neutron-skin thickness with free spectator neutrons in ultracentral high-energy isobaric collisions

We show that the yield ratio of free spectator neutrons produced in high-energy $^{96}$Zr+$^{96}$Zr to $^{96}$Ru+$^{96}$Ru collisions is a clean probe of the neutron-skin thickness of colliding nuclei and the slope parameter $L$ of the symmetry energy. The idea is demonstrated based on the proton and neutron density distributions via a state-of-the-art Skyrme-Hartree-Fock-Bogolyubov calculation. Among spectator nucleons given by the Glauber model, free spectator neutrons include those from direct production that survive from clusterization as well as those from deexcitation of heavy clusters described by the popular GEMINI model. More free neutrons are produced in collisions of $^{96}$Zr nucleus due to its larger neutron skin, compared to those produced in collisions of $^{96}$Ru nucleus with a smaller neutron skin. The difference of the free spectator neutron yield is further increased with the increasing difference of the neutron-skin thickness between $^{96}$Zr and $^{96}$Ru with a larger $L$ value, and the increase in ultracentral collisions is particularly insensitive to model details and experimental uncertainties. Since the production of free spectator neutrons is not affected by the complicated dynamics in the mid-rapidity region, the ratio of their multiplicities in ultracentral isobaric collisions is a robust observable for constraining the neutron skin and $L$ value.

nucl-th↗

arXiv2022

Free spectator nucleons in ultracentral relativistic heavy-ion collisions as a probe of neutron skin

Besides the yield ratio of free spectator neutrons produced in ultracentral $^{96}$Zr+$^{96}$Zr to $^{96}$Ru+$^{96}$Ru collisions, we propose that the yield ratio $N_n/N_p$ of free spectator neutrons to protons in a single collision system at RHIC and LHC can be a more sensitive probe of the neutron-skin thickness $Δr_{\mathrm{np}}$ and the slope parameter $L$ of the symmetry energy. The idea is demonstrated based on the proton and neutron density distributions of colliding nuclei obtained from Skyrme-Hartree-Fock-Bogolyubov calculations, and a Glauber model that provides information of spectator matter. The final spectator particles are produced from direct emission, clusterization by a minimum spanning tree algorithm or a Wigner function approach, and deexcitation of heavy clusters by GEMINI. A larger $Δr_{\mathrm{np}}$ associated with a larger $L$ value increases the isospin asymmetry of spectator matter and thus leads to a larger $N_n/N_p$, especially in ultracentral collisions where the multiplicity of free nucleons are free from the uncertainties of cluster formation and deexcitation. We have further shown that the double ratio of $N_n/N_p$ in isobaric collision systems or in collisions by isotopes helps to cancel the detecting efficiency for protons. Effects from nuclear deformation and electromagnetic excitation are studied, and they are found to be subdominant compared to the expected sensitivity to $Δr_{\mathrm{np}}$.

nucl-th↗

arXiv2022

A Brief History of Recommender Systems

Soon after the invention of the Internet, the recommender system emerged and related technologies have been extensively studied and applied by both academia and industry. Currently, recommender system has become one of the most successful web applications, serving billions of people in each day through recommending different kinds of contents, including news feeds, videos, e-commerce products, music, movies, books, games, friends, jobs etc. These successful stories have proved that recommender system can transfer big data to high values. This article briefly reviews the history of web recommender systems, mainly from two aspects: (1) recommendation models, (2) architectures of typical recommender systems. We hope the brief review can help us to know the dots about the progress of web recommender systems, and the dots will somehow connect in the future, which inspires us to build more advanced recommendation services for changing the world better.

cs.IR↗

arXiv2022

Simulating fusion reactions from Coulomb explosions within a transport approach

We have studied nuclear fusion reactions from the Coulomb explosion of deuterium clusters induced by high-intensity laser beams within a transport approach. By incorporating the D+D $\rightarrow$ n + He$^3$ channel as inelastic collisions based on the stochastic method, we have calibrated the neutron yield from the simulation in a box system with that from the reaction rate equation. After justifying the Coulomb explosion of a single cluster by comparing results with available theoretical limits, we have then investigated the dynamics from Coulomb explosions of systems with different cluster numbers and different deuteron numbers in clusters. We find that the kinetic energy spectrum of deuterons at the final stage is different from that when neutrons are abundantly produced, corresponding to significantly different reaction rates. We also extrapolate the neutron yield result from small systems to large systems based on an intuitive parameterized form and compare with the available experimental result. The present framework can be extended by incorporating more channels, and useful for further studies of nuclear fusion reactions in plasma systems at higher energies reached in more recent experiments.

nucl-th↗

arXiv2022

Error-free approximation of explicit linear MPC through lattice piecewise affine expression

In this paper, the disjunctive and conjunctive lattice piecewise affine (PWA) approximations of explicit linear model predictive control (MPC) are proposed. The training data are generated uniformly in the domain of interest, consisting of the state samples and corresponding affine control laws, based on which the lattice PWA approximations are constructed. Re-sampling of data is also proposed to guarantee that the lattice PWA approximations are identical to explicit MPC control law in the unique order (UO) regions containing the sample points as interior points. Additionally, under mild assumptions, the equivalence of the two lattice PWA approximations guarantees that the approximations are error-free in the domain of interest. The algorithms for deriving statistically error-free approximation to the explicit linear MPC are proposed and the complexity of the entire procedure is analyzed, which is polynomial with respect to the number of samples. The performance of the proposed approximation strategy is tested through two simulation examples, and the result shows that with a moderate number of sample points, we can construct lattice PWA approximations that are equivalent to optimal control law of the explicit linear MPC.

eess.SY↗

arXiv2022

Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis

Due to the constraints of the imaging device and high cost in operation time, computer tomography (CT) scans are usually acquired with low intra-slice resolution. Improving the intra-slice resolution is beneficial to the disease diagnosis for both human experts and computer-aided systems. To this end, this paper builds a novel medical slice synthesis to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy to accomplish this task in the self-supervised learning manner. Specifically, we model this problem from three different views: slice-wise interpolation from axial view and pixel-wise interpolation from coronal and sagittal views. Under this circumstance, the models learned from different views can distill valuable knowledge to guide the learning processes of each other. We can repeat this process to make the models synthesize intermediate slice data with increasing inter-slice resolution. To demonstrate the effectiveness of the proposed approach, we conduct comprehensive experiments on a large-scale CT dataset. Quantitative and qualitative comparison results show that our method outperforms state-of-the-art algorithms by clear margins.

eess.IV↗

arXiv2022

swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM achieves 1.79x on average compared to the state-of-the-art deep learning framework on Sunway, across six representative benchmarks. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in mind. We believe this work will encourage more people to embrace the power of deep learning and Sunway many-core processor.

cs.LG↗

arXiv2022

Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction

As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems. This task has a high demand on the explainability of matching results because of its critical impacts on downstream applications -- the matched legal cases may provide supportive evidence for the judgments of target cases and thus influence the fairness and justice of legal decisions. Focusing on this challenging task, we propose a novel and explainable method, namely \textit{IOT-Match}, with the help of computational optimal transport, which formulates the legal case matching problem as an inverse optimal transport (IOT) problem. Different from most existing methods, which merely focus on the sentence-level semantic similarity between legal cases, our IOT-Match learns to extract rationales from paired legal cases based on both semantics and legal characteristics of their sentences. The extracted rationales are further applied to generate faithful explanations and conduct matching. Moreover, the proposed IOT-Match is robust to the alignment label insufficiency issue commonly in practical legal case matching tasks, which is suitable for both supervised and semi-supervised learning paradigms. To demonstrate the superiority of our IOT-Match method and construct a benchmark of explainable legal case matching task, we not only extend the well-known Challenge of AI in Law (CAIL) dataset but also build a new Explainable Legal cAse Matching (ELAM) dataset, which contains lots of legal cases with detailed and explainable annotations. Experiments on these two datasets show that our IOT-Match outperforms state-of-the-art methods consistently on matching prediction, rationale extraction, and explanation generation.

cs.IR↗

arXiv2022

Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework

Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy. Ultrasound (US) is a viable alternative for RLN detection due to its safety and ability to provide real-time feedback. However, the tininess of the RLN, with a diameter typically less than 3mm, poses significant challenges to the accurate localization of the RLN. In this work, we propose a knowledge-driven framework for RLN localization, mimicking the standard approach surgeons take to identify the RLN according to its surrounding organs. We construct a prior anatomical model based on the inherent relative spatial relationships between organs. Through Bayesian shape alignment (BSA), we obtain the candidate coordinates of the center of a region of interest (ROI) that encloses the RLN. The ROI allows a decreased field of view for determining the refined centroid of the RLN using a dual-path identification network, based on multi-scale semantic information. Experimental results indicate that the proposed method achieves superior hit rates and substantially smaller distance errors compared with state-of-the-art methods.

eess.IV↗

arXiv2022

Dynamic Message Propagation Network for RGB-D Salient Object Detection

This paper presents a novel deep neural network framework for RGB-D salient object detection by controlling the message passing between the RGB images and depth maps on the feature level and exploring the long-range semantic contexts and geometric information on both RGB and depth features to infer salient objects. To achieve this, we formulate a dynamic message propagation (DMP) module with the graph neural networks and deformable convolutions to dynamically learn the context information and to automatically predict filter weights and affinity matrices for message propagation control. We further embed this module into a Siamese-based network to process the RGB image and depth map respectively and design a multi-level feature fusion (MFF) module to explore the cross-level information between the refined RGB and depth features. Compared with 17 state-of-the-art methods on six benchmark datasets for RGB-D salient object detection, experimental results show that our method outperforms all the others, both quantitatively and visually.

cs.CV↗

arXiv2022

Person-job fit estimation from candidate profile and related recruitment history with co-attention neural networks

Existing online recruitment platforms depend on automatic ways of conducting the person-job fit, whose goal is matching appropriate job seekers with job positions. Intuitively, the previous successful recruitment records contain important information, which should be helpful for the current person-job fit. Existing studies on person-job fit, however, mainly focus on calculating the similarity between the candidate resumes and the job postings on the basis of their contents, without taking the recruiters' experience (i.e., historical successful recruitment records) into consideration. In this paper, we propose a novel neural network approach for person-job fit, which estimates person-job fit from candidate profile and related recruitment history with co-attention neural networks (named PJFCANN). Specifically, given a target resume-job post pair, PJFCANN generates local semantic representations through co-attention neural networks and global experience representations via graph neural networks. The final matching degree is calculated by combining these two representations. In this way, the historical successful recruitment records are introduced to enrich the features of resumes and job postings and strengthen the current matching process. Extensive experiments conducted on a large-scale recruitment dataset verify the effectiveness of PJFCANN compared with several state-of-the-art baselines. The codes are released at: https://github.com/CCIIPLab/PJFCANN.

cs.IR↗