arXiv subjects

Chang Gong

Publications and source records attributed to Chang Gong.

18 recordsLinked to original sources

arXiv2026

Low-energy Muon-Nucleon scattering experiment: LUNE (White Paper)

The HIAF will provide high-intensity, high-quality muon beams with momenta from 0.5 to 7.5 GeV/c. This energy range is uniquely suited for precision muon scattering, bridging the gap between low-energy electron facilities and future high-energy lepton-ion colliders. In particular, HIAF will enable precision measurements with both positive and negative muon beams over a broad kinematic range, complementing existing electron-scattering facilities such as JLab, EicC and EIC. Based on HIAF muon source, the LUNE Collaboration has been established to address several fundamental questions in nuclear and particle physics, including the proton charge radius puzzle, nucleon electromagnetic structure, and the dynamics of quantum electrodynamics and hadronic interactions. The program proceeds in two phases, from elastic scattering to nucleon structure and beyond-Standard-Model searches. The experiment is expected to determine the proton charge radius with a precision of approximately 1.0\% using elastic muon-proton scattering. It will also perform systematic measurements of the proton electromagnetic form factors with both $\mu^+$ and $\mu^-$ beams, enabling precise studies of two-photon exchange effects and stringent tests of quantum electrodynamics. Beyond elastic scattering, LUNE will investigate TMD, gravitational form factors, and nuclear charge radii, providing new insights into the 3D structure of nucleons and nuclei. The experiment will further address important topics including Coulomb-distortion corrections, nuclear medium effects, and possible signatures of physics beyond the Standard Model. This white paper presents the scientific motivation, detector concept, expected performance, and long-term strategy of LUNE.

hep-ex↗

arXiv2025

GTool: Graph Enhanced Tool Planning with Large Language Model

Tool planning with large language models (LLMs), referring to selecting, organizing, and preparing the tools necessary to complete a user request, bridges the gap between natural language understanding and task execution. However, current works treat different tools as isolated components and fail to leverage the inherent dependencies of tools, leading to invalid planning results. Since tool dependencies are often incomplete, it becomes challenging for LLMs to accurately identify the appropriate tools required by a user request, especially when confronted with a large toolset. To solve this challenge, we propose \texttt{GTool}, which is the first work aiming to enhance the tool planning ability of LLMs under incomplete dependencies. \texttt{GTool} constructs a request-specific tool graph to select tools efficiently and generate the \texttt{ } which provides sufficient dependency information understandable by LLMs. Moreover, a missing dependency prediction task is designed to improve the reliability of \texttt{GTool} with incomplete dependencies. Without trimming LLMs, \texttt{GTool} can be seamlessly integrated with various LLM backbones without extensive retraining. Extensive experiments show that \texttt{GTool} achieves more than 29.6\% performance improvements compared with the state-of-the-art (SOTA) baselines with a light-weight (7B) LLM backbone.

cs.AI↗

arXiv2025

Information Security Based on LLM Approaches: A Review

Information security is facing increasingly severe challenges, and traditional protection means are difficult to cope with complex and changing threats. In recent years, as an emerging intelligent technology, large language models (LLMs) have shown a broad application prospect in the field of information security. In this paper, we focus on the key role of LLM in information security, systematically review its application progress in malicious behavior prediction, network threat analysis, system vulnerability detection, malicious code identification, and cryptographic algorithm optimization, and explore its potential in enhancing security protection performance. Based on neural networks and Transformer architecture, this paper analyzes the technical basis of large language models and their advantages in natural language processing tasks. It is shown that the introduction of large language modeling helps to improve the detection accuracy and reduce the false alarm rate of security systems. Finally, this paper summarizes the current application results and points out that it still faces challenges in model transparency, interpretability, and scene adaptability, among other issues. It is necessary to explore further the optimization of the model structure and the improvement of the generalization ability to realize a more intelligent and accurate information security protection system.

cs.CR↗

arXiv2025

Pseudocode-Injection Magic: Enabling LLMs to Tackle Graph Computational Tasks

Graph computational tasks are inherently challenging and often demand the development of advanced algorithms for effective solutions. With the emergence of large language models (LLMs), researchers have begun investigating their potential to address these tasks. However, existing approaches are constrained by LLMs' limited capability to comprehend complex graph structures and their high inference costs, rendering them impractical for handling large-scale graphs. Inspired by human approaches to graph problems, we introduce a novel framework, PIE (Pseudocode-Injection-Enhanced LLM Reasoning for Graph Computational Tasks), which consists of three key steps: problem understanding, prompt design, and code generation. In this framework, LLMs are tasked with understanding the problem and extracting relevant information to generate correct code. The responsibility for analyzing the graph structure and executing the code is delegated to the interpreter. We inject task-related pseudocodes into the prompts to further assist the LLMs in generating efficient code. We also employ cost-effective trial-and-error techniques to ensure that the LLM-generated code executes correctly. Unlike other methods that require invoking LLMs for each individual test case, PIE only calls the LLM during the code generation phase, allowing the generated code to be reused and significantly reducing inference costs. Extensive experiments demonstrate that PIE outperforms existing baselines in terms of both accuracy and computational efficiency.

cs.CL↗

arXiv2024

CausalTAD: Causal Implicit Generative Model for Debiased Online Trajectory Anomaly Detection

Trajectory anomaly detection, aiming to estimate the anomaly risk of trajectories given the Source-Destination (SD) pairs, has become a critical problem for many real-world applications. Existing solutions directly train a generative model for observed trajectories and calculate the conditional generative probability $P({T}|{C})$ as the anomaly risk, where ${T}$ and ${C}$ represent the trajectory and SD pair respectively. However, we argue that the observed trajectories are confounded by road network preference which is a common cause of both SD distribution and trajectories. Existing methods ignore this issue limiting their generalization ability on out-of-distribution trajectories. In this paper, we define the debiased trajectory anomaly detection problem and propose a causal implicit generative model, namely CausalTAD, to solve it. CausalTAD adopts do-calculus to eliminate the confounding bias of road network preference and estimates $P({T}|do({C}))$ as the anomaly criterion. Extensive experiments show that CausalTAD can not only achieve superior performance on trained trajectories but also generally improve the performance of out-of-distribution data, with improvements of $2.1\% \sim 5.7\%$ and $10.6\% \sim 32.7\%$ respectively.

cs.LG↗

arXiv2024

$J/\psi$ photoproduction near threshold and signals for the hidden charm pentaquarks

We study the $J/\psi$ photoproduction $\gamma p\to J/\psi p$ near threshold to investigate the role played by the open charm channels and possible signals from the hidden charm $P_c$ pentaquark states. With the diffractive mechanism described by a Pomeron exchange model extrapolated from high energies to the lower energy region, it shows that the differential cross sections compared with the recent results from the GlueX Collaboration have apparent deficits in the large scattering angles. The inclusion of the open charm channels and intermediate $P_c$ states can significantly improve the descriptions of the differential cross section data, in particular, at the energy regions of the $\Lambda_c\bar{D}^{(*)}$ thresholds. This can explain the structures observed by the GlueX Collaboration at the $\Lambda_c\bar{D}^{(*)}$ thresholds as the open charm CUSP effects. Given that these $P_c$ pentaquark states are hadronic molecules dynamically generated by the $\Sigma_c\bar{D}^{(*)}$, we find that the production of these $P_c$ states should be suppressed at leading order since their couplings to both $\gamma p$ and $J/\psi p$ are through loop transitions. This can explain why no obvious signals for the $P_c$ states are observed by the present datasets. In order to further disentangling the role played by $s$-channel mechanisms, we also investigate the polarized beam asymmetry which shows sensitivities to the open charm threshold effects and interferences from the $P_c$ productions. Experimental measurement of this observable at the Jefferson Laboratory is strongly encouraged.

hep-ph↗

arXiv2024

PORCA: Root Cause Analysis with Partially Observed Data

Root Cause Analysis (RCA) aims at identifying the underlying causes of system faults by uncovering and analyzing the causal structure from complex systems. It has been widely used in many application domains. Reliable diagnostic conclusions are of great importance in mitigating system failures and financial losses. However, previous studies implicitly assume a full observation of the system, which neglect the effect of partial observation (i.e., missing nodes and latent malfunction). As a result, they fail in deriving reliable RCA results. In this paper, we unveil the issues of unobserved confounders and heterogeneity in partial observation and come up with a new problem of root cause analysis with partially observed data. To achieve this, we propose PORCA, a novel RCA framework which can explore reliable root causes under both unobserved confounders and unobserved heterogeneity. PORCA leverages magnified score-based causal discovery to efficiently optimize acyclic directed mixed graph under unobserved confounders. In addition, we also develop a heterogeneity-aware scheduling strategy to provide adaptive sample weights. Extensive experimental results on one synthetic and two real-world datasets demonstrate the effectiveness and superiority of the proposed framework.

cs.AI↗

arXiv2024

STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis

The rapid evolution of large language models (LLMs) holds promise for reforming the methodology of spatio-temporal data mining. However, current works for evaluating the spatio-temporal understanding capability of LLMs are somewhat limited and biased. These works either fail to incorporate the latest language models or only focus on assessing the memorized spatio-temporal knowledge. To address this gap, this paper dissects LLMs' capability of spatio-temporal data into four distinct dimensions: knowledge comprehension, spatio-temporal reasoning, accurate computation, and downstream applications. We curate several natural language question-answer tasks for each category and build the benchmark dataset, namely STBench, containing 13 distinct tasks and over 60,000 QA pairs. Moreover, we have assessed the capabilities of 13 LLMs, such as GPT-4o, Gemma and Mistral. Experimental results reveal that existing LLMs show remarkable performance on knowledge comprehension and spatio-temporal reasoning tasks, with potential for further enhancement on other tasks through in-context learning, chain-of-though prompting, and fine-tuning. The code and datasets of STBench are released on https://github.com/LwbXc/STBench.

cs.CL↗

arXiv2024

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

In online advertising, marketing mix modeling (MMM) is employed to predict the gross merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget allocation of various advertising channels. Traditional MMM methods leveraging regression techniques can fail in handling the complexity of marketing. Although some efforts try to encode the causal structures for better prediction, they have the strict restriction that causal structures are prior-known and unchangeable. In this paper, we define a new causal MMM problem that automatically discovers the interpretable causal structures from data and yields better GMV predictions. To achieve causal MMM, two essential challenges should be addressed: (1) Causal Heterogeneity. The causal structures of different kinds of shops vary a lot. (2) Marketing Response Patterns. Various marketing response patterns i.e., carryover effect and shape effect, have been validated in practice. We argue that causal MMM needs dynamically discover specific causal structures for different shops and the predictions should comply with the prior known marketing response patterns. Thus, we propose CausalMMM that integrates Granger causality in a variational inference framework to measure the causal relationships between different channels and predict the GMV with the regularization of both temporal and saturation marketing response patterns. Extensive experiments show that CausalMMM can not only achieve superior performance of causal structure learning on synthetic datasets with improvements of 5.7%\sim 7.1%, but also enhance the GMV prediction results on a representative E-commerce platform.

cs.AI↗

arXiv2023

Coupled-channel description of charmed heavy hadronic molecules within the meson-exchange model and its implication

Motivated by the first observation of the double-charm tetraquark $T_{cc}^+(3875)$ by the LHCb Collaboration, we investigate the nature of $T_{cc}^+$ as an isoscalar $DD^*$ hadronic molecule in a meson-exchange potential model incorporated by the coupled-channel effects and three-body unitarity. The $D^0D^0\pi^+$ invariant mass spectrum can be well-described and the $T_{cc}^+$ pole structure can be precisely extracted. Under the hypothesis that the interactions between the heavy flavor hadrons can be saturated by the light meson-exchange potentials, the near-threshold dynamics of $T_{cc}^+$ can shed light on the binding of its heavy-quark spin symmetry (HQSS) partner $D^*D^*$ ($I=0$) and on the nature of other heavy hadronic molecule candidates such as $X(3872)$ and $Z_c(3900)$ in the charmed-anticharmed systems. The latter states can be related to $T_{cc}^+$ in the meson-exchange potential model with limited assumptions based on the SU(3) flavor symmetry relations. The combined analysis, on the one hand, indicates the HQSS breaking effects among those HQSS partners, and on the other hand, highlights the role played by the short and long-distance dynamics for the near threshold $D^{(*)}D^{(*)}$ and $D^{(*)}\bar{D}^{(*)}+c.c.$ systems.

hep-ph↗

arXiv2023

Causal Discovery from Temporal Data: An Overview and New Perspectives

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and prediction, have been proposed in the past decades. Among them, causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task and has attracted much research attention. Existing causal discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series causal discovery, and event sequence causal discovery. However, most previous surveys are only focused on the time series causal discovery and ignore the second category. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data causal discovery.

cs.LG↗

arXiv2022

CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution

Multi-touch attribution (MTA), aiming to estimate the contribution of each advertisement touchpoint in conversion journeys, is essential for budget allocation and automatically advertising. Existing methods first train a model to predict the conversion probability of the advertisement journeys with historical data and calculate the attribution of each touchpoint using counterfactual predictions. An assumption of these works is the conversion prediction model is unbiased, i.e., it can give accurate predictions on any randomly assigned journey, including both the factual and counterfactual ones. Nevertheless, this assumption does not always hold as the exposed advertisements are recommended according to user preferences. This confounding bias of users would lead to an out-of-distribution (OOD) problem in the counterfactual prediction and cause concept drift in attribution. In this paper, we define the causal MTA task and propose CausalMTA to eliminate the influence of user preferences. It systemically eliminates the confounding bias from both static and dynamic preferences to learn the conversion prediction model using historical data. We also provide a theoretical analysis to prove CausalMTA can learn an unbiased prediction model with sufficient data. Extensive experiments on both public datasets and the impression data in an e-commerce company show that CausalMTA not only achieves better prediction performance than the state-of-the-art method but also generates meaningful attribution credits across different advertising channels.

cs.IR↗

arXiv2022

Pseudoscalar charmonium pair interactions via the Pomeron exchange mechanism

Following the observation of the fully-heavy tetraquark candidates $X(6900)$ and possible $X(6300)$ in the di-$J/ψ$ spectrum by the LHCb Collaboration, we investigate the near-threshold dynamics of the pseudoscalar quarkonium pairs in a coupled-channel approach in the di-$η_c$ channel. We show that the Pomeron exchange mechanism should be a general dynamics in the near-threshold heavy quarkonium pair interactions. In the di-$η_c$ channel, a coupled-channel system involving the di-$η_c$, $η_c$-$η_c(2S)$ and di-$η_c(2S)$ interactions can be established. Their $S$-wave interactions near threshold via the Pomeron exchange potential can produce near-threshold resonance poles similar to the di-$J/ψ$, $J/ψ$-$ψ(2S)$ and di-$ψ(2S)$ coupled channels. Interestingly, we find here that the pole positions are more shifted to the corresponding thresholds and turn out to be different from those seen in the di-$J/ψ$ spectrum. Taking into account the suppression of the heavy quark spin flips in the transitions between two vector and two pseudoscalar heavy quarkonium systems, the enhancements in the di-$η_c$ spectrum should be different from those seen in the di-$J/ψ$ channel. Experimental study of the di-$η_c$ channel should be useful for disentangling the nature of the fully-heavy tetraquark systems.

hep-ph↗

arXiv2021

Nature of $X(6900)$ and its production mechanism at LHCb

We show that the Pomeron exchanges play a unique role in vector charmonium scatterings. Such a mechanism can provide a natural explanation for the nontrivial structures in the di-$J/ψ$ spectrum observed by the LHCb Collaboration. The narrow structure $X(6900)$, as a dynamically generated resonance pole, can arise from the Pomeron exchanges and coupled-channel effects between the $J/ψ$-$J/ψ$, $J/ψ$-$ψ(2S)$ scatterings. A pole structure near the di-$J/ψ$ threshold is also found. Meanwhile, we predict that $X(6900)$ can produce significant threshold enhancement in the $J/ψ$-$ψ(2S)$ energy spectrum which can be searched for at LHCb.

hep-ph↗

arXiv2021

Electron-Ion Collider in China

Lepton scattering is an established ideal tool for studying inner structure of small particles such as nucleons as well as nuclei. As a future high energy nuclear physics project, an Electron-ion collider in China (EicC) has been proposed. It will be constructed based on an upgraded heavy-ion accelerator, High Intensity heavy-ion Accelerator Facility (HIAF) which is currently under construction, together with a new electron ring. The proposed collider will provide highly polarized electrons (with a polarization of $\sim$80%) and protons (with a polarization of $\sim$70%) with variable center of mass energies from 15 to 20 GeV and the luminosity of (2-3) $\times$ 10$^{33}$ cm$^{-2}$ s$^{-1}$. Polarized deuterons and Helium-3, as well as unpolarized ion beams from Carbon to Uranium, will be also available at the EicC. The main foci of the EicC will be precision measurements of the structure of the nucleon in the sea quark region, including 3D tomography of nucleon; the partonic structure of nuclei and the parton interaction with the nuclear environment; the exotic states, especially those with heavy flavor quark contents. In addition, issues fundamental to understanding the origin of mass could be addressed by measurements of heavy quarkonia near-threshold production at the EicC. In order to achieve the above-mentioned physics goals, a hermetical detector system will be constructed with cutting-edge technologies. This document is the result of collective contributions and valuable inputs from experts across the globe. The EicC physics program complements the ongoing scientific programs at the Jefferson Laboratory and the future EIC project in the United States. The success of this project will also advance both nuclear and particle physics as well as accelerator and detector technology in China.

nucl-ex↗

arXiv2018

Nuclear EMC effect through $\barΛ/Λ$ production in semi-inclusive deep-inelastic scattering processes

We calculate $Λ$ and $\barΛ$ hadron production cross sections in charged lepton semi-inclusive deep-inelastic scattering off nuclear target ($A$, using iron $\mathrm{Fe}$ as an example) and deuteron ($D$) target. The results show that the ratio $(\barΛ^{A}/Λ^{A})/(\barΛ^{D}/Λ^{D})$ is sensitive to the sea quark content of the nucleus. We adopt three different models to take the nuclear EMC effect into account. The ratio $(\barΛ^{A}/Λ^{A})/(\barΛ^{D}/Λ^{D})$ is predicted to be different by these different models.

nucl-th↗

arXiv2017

Effect of sea quarks on single-spin asymmetries $A^{W^{\pm}}_{N}$ in transversely polarized pp collisions at RHIC

We calculate the single-spin asymmetries $A^{W^{\pm}}_{N}$ of $W^{\pm}$ bosons produced in transversely polarized pp collisions with the valence part of the up (u) and down (d) quark Sivers functions treated by an available parametrization and the light-cone quark spectator-diquark model respectively, while the sea part Sivers functions of u and d quarks treated as parametrization. Comparing our results with those from experimental data at RHIC, we find that the Sivers functions of sea quarks play an important role in the determination of the shapes of $A^{W^{\pm}}_{N}$. It is shown that $A^{W^{-}}_{N}$ is sensitive to u sea Sivers function, while $A^{W^{+}}_{N}$ to d sea Sivers function intuitively. The results show that the contributions of u and d sea Sivers functions are rather sizable and of the same sign, and their signs agree with that of d valence quarks and are opposite to that of u valence quarks.

hep-ph↗

arXiv2017

Effect of sea quarks on the single-spin asymmetries $A^{W^{\pm}}_{L}$ in polarized pp collisions at RHIC

We calculate the single-spin asymmetries $A^{W^{\pm}}_{L}$ of $W^{\pm}$ bosons produced in polarized pp collisions with the valence part of the up and down quark helicity distributions modeled by the light-cone quark-spectator-diquark model while the sea part helicity distributions of the up and down quarks treated as parametrization. Comparing our results with those from experimental data at RHIC, we find that the helicity distributions of sea quarks play an important role in the determination of the shapes of $A^{W^{\pm}}_{L}$. It is shown that $A^{W^{-}}_{L}$ is sensitive to $Δ\bar u$, while $A^{W^{+}}_{L}$ to $Δ\bar d$ intuitively. The experimental data of the polarized structure functions and the sum of helicities are also important to constrain the sizes of quark helicity distributions both for the sea part and the valence part of the nucleon.

hep-ph↗