arXiv subjects

Xuejian Wang

Publications and source records attributed to Xuejian Wang.

8 recordsLinked to original sources

arXiv2026

$\rm S^*(E)$ measurement of the $\rm {}^{12}C({}^{12}C,α){}^{20}Ne$ reaction at astrophysical energies via the Trojan horse method with $\rm ^{16}O$ quasi-free breakup

The 12C(12C,a)20Ne reaction at astrophysical energies is crucial for understanding the carbon burning process in massive star and explosive astrophysical scenarios like Type Ia supernovae and X-ray bursts. However, directly measuring or simply extrapolating its S*(E) factor is extremely challenging due to Coulomb suppression and potential complex resonance structures near the Gamow window (1.5+-0.3 MeV). The THM can circumvent the Coulomb barrier, providing data within the Gamow window without extrapolation. Strong resonances near 1.5 MeV were previously reported by Tumino et al. using THM with 14N=(12C+d), a result that generated significant interest and debate, underscoring the need for further experimental verification. In this work, we selected 16O=(12C+a) as the Trojan-horse nucleus due to its lower binding energy, which favors quasi-free reactions. We performed an indirect measurement of 12C(16O,aa)20Ne at the HI-13 Tandem Accelerator at CIAE. Employing a copper beam-stopper foil, we measured the spectator a-particle within a small angular range around 0, where the quasi-free mechanism predicts its highest concentration. The S*(E) factor of 12C(12C,a)20Ne in the astrophysical energy region was extracted from the measured three-body reaction using THM based on DWBA. Our results confirm the existence of resonances within the Gamow window around 1.5 MeV in both the a0 and a1 channels. Without considering the details of the resonance structures, the overall trend of our results is qualitatively in reasonable agreement with the THM-Tumino2018 and TTIK2025 data, but differs significantly from the trend of the Modified-THM-Muk2019 data. We observe no evidence for hindrance effect in our results.

nucl-ex↗

arXiv2026

Indirect Measurement of the $\rm S^*(E)$ Factor for $\rm {}^{12}C({}^{12}C,\mathit{p}){}^{23}Na$ at Gamow Energies via the Trojan Horse Method with Near-0 degree Spectator Detection

The astrophysical S*(E) factor for the 12C+12C reaction within the Gamow window plays a pivotal role in modeling stellar carbon burning and explosive nucleosynthesis scenarios. However, direct measurements or even simple extrapolations at these energies are severely hindered by Coulomb suppression and the possible presence of narrow resonances. To address this challenge, we performed an indirect measurement of the 12C(16O,ap)23Na reaction at the HI-13 Tandem Accelerator, employing 16O=(12C+a) as the Trojan Horse nucleus. A key innovation of this Trojan Horse Method (THM) study is the implementation of a copper beam-stopper foil, which enabled the detection of spectator particles near 0, the angular region where their yield is maximized under quasi-free kinematics. The S*(E) factor for the 12C(12C,p)23Na reaction in the astrophysically relevant energy range was extracted using the THM formalism based on the DWBA. Our results confirm the presence of resonant structures within the Gamow window around 1.5 MeV in both the p0 and p1 proton channels. No evidence of a hindrance effect is observed in the measured energy range. Without considering the resonance details, the overall trend of our results is qualitatively in reasonable agreement with the THM-Tumino2018 and TTIK2025 data, but differs significantly from the trend of the Modified-THM-Muk2019 data.

nucl-ex↗

arXiv2026

Multiscale Modelling of Ferroelectrics using a Physics-Informed Neural Network Driven by Molecular Dynamics Data: Parameter Identification and Field Reconstruction

In multiscale modeling of ferroelectrics, combining atomistic simulation with continuum-scale phase-field models (PFM) remains a fundamental challenge. A key difficulty lies in faithfully capturing discrete atomic-level information within a continuum modeling framework, while accurately representing material behavior at the mesoscale. In this paper, a Physics-Informed Neural Network (PINN) driven by molecular dynamics (MD) data is used. The loss function of the network consists of a supervised term that fits the discrete spatial polarization distributions obtained from MD simulations of systems containing domain walls, and a physics-based term that incorporates the residuals of partial differential equations (PDEs) of steady-state PFM. To ensure stable and balanced training among the different loss components, adaptive gradient normalization (GradNorm) is used to dynamically adjust the task weights. By minimizing the total loss, the model not only reconstructs the polarization field along with the associated strain, stress, and energy landscape at the continuum scale, but also identifies critical physical parameters of the phase-field model, including the characteristic energy density, characteristic length factor, gradient energy anisotropy factor, and Landau polynomial coefficients. By using the PINN-predicted physical parameters in COMSOL Multiphysics to solve the corresponding PDEs within a finite element framework, we demonstrate that these parameters enable accurate reproduction of the ferroelectric domain structure and the associated material response, including stress/strain distributions and energy landscape. This framework provides an effective methodology for establishing multiscale connections between atomistic and continuum descriptions, and holds the potential to infer underlying physical properties directly from polarization distributions for a wide range of materials.

cond-mat.mtrl-sci↗

arXiv2020

Product Differentiation and Geographical Expansion of Exports Network at Industry level

Industries can enter one country first, and then enter its neighbors' markets. Firms in the industry can expand trade network through the export behavior of other firms in the industry. If a firm is dependent on a few foreign markets, the political risks of the markets will hurt the firm. The frequent trade disputes reflect the importance of the choice of export destinations. Although the market diversification strategy was proposed before, most firms still focus on a few markets, and the paper shows reasons.In this paper, we assume the entry cost of firms is not all sunk cost, and show 2 ways that product heterogeneity impacts extensive margin of exports theoretically and empirically. Firstly, the increase in product heterogeneity promotes the increase in market power and profit, and more firms are able to pay the entry cost. If more firms enter the market, the information of the market will be known by other firms in the industry. Firms can adjust their behavior according to other firms, so the information changes entry cost and is not sunk cost completely. The information makes firms more likely to entry the market, and enter the surrounding markets of existing markets of other firms in the industry. When firms choose new markets, they tend to enter the markets with few competitors first.Meanwhile, product heterogeneity will directly affect the firms' network expansion, and the reduction of product heterogeneity will increase the value of peer information. This makes firms more likely to entry the market, and firms in the industry concentrate on the markets.

econ.GN↗

arXiv2020

Impact of Regional Reactions to War on Contemporary Chinese Trade

Different regional reactions to war in 1894 and 1900 can significantly impact Chinese imports in 2001. As international relationship gets tense and China rises, international conflicts could decrease trade.We analyze impact of historic political conflict. We measure regional change of number of people passing imperial exam because of war. War leads to an unsuccessful reform and shocks elites. Elites in different regions have different ideas about modernization, and the change of number of people passing exam is quite different in different regions after war. Regional number of people passing exam increases 1% after war, imports from then empires decrease 2.050% in 2001, and this shows impact of cultural barrier. Manufactured goods can be impacted because brands can be identified easily. Risk aversion of expensive products in conservative regions can increase imports of equipment. Value chains need deep trust, and this decreases imports of foreign company and assembly trade.

econ.GN↗

arXiv2019

Combining Machine Learning Models using combo Library

Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. To facilitate this process, we propose and implement an easy-to-use Python toolkit, combo, to aggregate models and scores under various scenarios, including classification, clustering, and anomaly detection. In a nutshell, combo provides a unified and consistent way to combine both raw and pretrained models from popular machine learning libraries, e.g., scikit-learn, XGBoost, and LightGBM. With accessibility and robustness in mind, combo is designed with detailed documentation, interactive examples, continuous integration, code coverage, and maintainability check; it can be installed easily through Python Package Index (PyPI) or https://github.com/yzhao062/combo.

cs.LG↗

arXiv2019

Continual Rare-Class Recognition with Emerging Novel Subclasses

Given a labeled dataset that contains a rare (or minority) class of of-interest instances, as well as a large class of instances that are not of interest, how can we learn to recognize future of-interest instances over a continuous stream? We introduce RaRecognize, which (i) estimates a general decision boundary between the rare and the majority class, (ii) learns to recognize individual rare subclasses that exist within the training data, as well as (iii) flags instances from previously unseen rare subclasses as newly emerging. The learner in (i) is general in the sense that by construction it is dissimilar to the specialized learners in (ii), thus distinguishes minority from the majority without overly tuning to what is seen in the training data. Thanks to this generality, RaRecognize ignores all future instances that it labels as majority and recognizes the recurrent as well as emerging rare subclasses only. This saves effort at test time as well as ensures that the model size grows moderately over time as it only maintains specialized minority learners. Through extensive experiments, we show that RaRecognize outperforms state-of-the art baselines on three real-world datasets that contain corporate-risk and disaster documents as rare classes.

cs.LG↗

arXiv2018

Large-scale Interactive Recommendation with Tree-structured Policy Gradient

Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to recommend (i.e., thousands of actions), most existing RL-based methods, however, fail to handle such a large discrete action space problem and thus become inefficient. The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action representation (the output of the actor network) and the real discrete action. To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree. Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods.

cs.LG↗