arXiv subjects

Jiamin Liu

Publications and source records attributed to Jiamin Liu.

At least 19 recordsLinked to original sources

arXiv2026

Probing hot QCD medium with heavy quarkonium in small and large collision systems

The yield ratios of different heavy-quarkonium states serve as sensitive probes of final-state interactions in relativistic nuclear collisions, because common cold-nuclear-matter effects are expected to be substantially reduced in these ratios. To quantify hot QCD medium effects in small collision systems, such as proton-nucleus collisions, we employ a time-dependent Schrodinger equation framework to consistently simulate the real-time evolution of both bottomonium and charmonium states in the presence of in-medium complex heavy-quark potentials. In p-Pb collisions at sqrt(sNN) = 8.16 TeV, our model successfully describes the observed suppression in the yield ratios of excited-to-ground states, specifically Upsilon(nS)/Upsilon(1S) and psi(2S)/J/psi, as a function of charged-particle multiplicity. This agreement supports the formation of a transient, hot QCD medium in small systems. Furthermore, the framework is employed to study the ratio of bottomonium nuclear modification factors in sqrt(sNN) = 5.02 TeV Pb-Pb collisions, where hot medium effects become stronger. By establishing a unified description across two distinct heavy-quark flavors and different collision systems, our study indicates that the yield ratio of bottomonium states serves as a comparatively clean probe of the hot QCD medium generated in small collision systems.

nucl-th↗

arXiv2026

Effects of event-by-event hydrodynamic fluctuations on bottomonium dynamics in Pb--Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV

We investigate the effects of event-by-event hydrodynamic fluctuations on bottomonium nuclear modification factors and elliptic flow in Pb--Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV. The internal evolution of the heavy quarkonium is described by a time-dependent Schr\"odinger equation with a temperature-dependent complex heavy-quark potential, while the hot QCD medium evolution is simulated using the iEBE-VISHNU event-by-event viscous hydrodynamic framework. By incorporating both fluctuating and smooth hot media, we find that the bottomonium nuclear modification factor $R_{AA}$ is only marginally affected by event-by-event fluctuations, whereas the elliptic flow $v_2$ is systematically enhanced, with the enhancement growing from the tightly bound $\Upsilon(1S)$ to the more weakly bound $\Upsilon(2S)$ and $\Upsilon(3S)$. This enhancement arises from the more pronounced participant-plane anisotropy of the fluctuating medium relative to the smooth optical-Glauber reference geometry. These results indicate that a smooth hydrodynamic background reproduces the bottomonium $R_{AA}$ but underestimates its $v_2$, so that the bottomonium $v_2$ retains a discernible imprint of event-by-event medium fluctuations.

nucl-th↗

arXiv2026

Unified Extraction of In-Medium Heavy Quark Potentials from RHIC to LHC Energies via Deep Learning

We use deep learning under Bayesian perspective to quantitatively extract the in-medium heavy quark (HQ) potential from bottomonium nuclear modification factors ($R_{AA}$) measured across multiple heavy ion collision systems at the Large Hadron Collider (LHC) and the Relativistic Heavy-Ion Collider (RHIC). The in-medium HQ potential, comprising both a real and imaginary part, is parameterized and incorporated into a time-dependent Schrödinger equation to model the wave function evolution of $b\bar{b}$ dipoles within a hydrodynamically evolving hot QCD medium. We construct Convolutional Neural Networks (CNNs) to capture the non-linear correspondence between the heavy quark potential $V(T,r)$ and the bottomonium $R_{AA}$ for Pb-Pb collisions at 5.02 TeV and 2.76 TeV, and Au-Au collisions at 200 GeV. Training datasets are generated by sampling the potential parameters and are further augmented using Principal Component Analysis (PCA) and Gaussian Process Regression (GPR). After validating the stability and correctness of the CNNs, we employ Stochastic Gradient Langevin Dynamics (SGLD) to perform a simultaneous Bayesian inverse extraction of the optimal potential parameters and their posterior distributions using experimental data of bottomonium $R_{AA}$ in both LHC and RHIC energies. Our joint multi-energy extraction suggests that, within the present parametrization and hydrodynamic background, the real part of the in-medium potential remains close to the vacuum Cornell form, corresponding to a relatively weak screened Debye mass across RHIC to LHC energies. By contrast, the imaginary part is more strongly constrained by the data and provides the dominant contribution to bottomonium suppression from RHIC to LHC energies.

nucl-th↗

arXiv2026

A Proposal-Free Query-Guided Network for Grounded Multimodal Named Entity Recognition

Grounded Multimodal Named Entity Recognition (GMNER) identifies named entities, including their spans and types, in natural language text and grounds them to the corresponding regions in associated images. Most existing approaches split this task into two steps: they first detect objects using a pre-trained general-purpose detector and then match named entities to the detected objects. However, these methods face a major limitation. Because pre-trained general-purpose object detectors operate independently of textual entities, they tend to detect common objects and frequently overlook specific fine-grained regions required by named entities. This misalignment between object detectors and entities introduces imprecision and can impair overall system performance. In this paper, we propose a proposal-free Query-Guided Network (QGN) that unifies multimodal reasoning and decoding through text guidance and cross- modal interaction. QGN enables accurate grounding and robust performance in open-domain scenarios. Extensive experiments demonstrate that QGN achieves top performance among compared GMNER models on widely used benchmarks.

cs.CV↗

arXiv2026

Nuclear Deformation Effects on Charmonium Suppression in Au+Au and U+U Collisions

We investigate the impact of intrinsic nuclear deformation and orientation on the yield suppression and momentum anisotropy of charmonia in Au+Au and U+U collisions at the Relativistic Heavy-Ion Collider. The anisotropic nucleon density within the nucleus is parameterized using a modified Woods-Saxon distribution, which is incorporated into the initial distributions of both the heavy quarkonia and the bulk medium energy density. The well-established Boltzmann-type transport equation is utilized to describe the dynamical evolution of quarkonium in the anisotropic bulk medium. Treating quarkonium suppression in Au+Au collisions as a baseline, we find that the momentum-integrated charmonium yield suppression is relatively insensitive to the initial nuclear geometry in deformed U+U collisions. In contrast, the anisotropic flow coefficients ($v_n$) of the charmonium is more sensitive to the nuclear deformation. Furthermore, these observables are also connected with the collision configuration, particularly when distinguishing between tip-tip and body-body orientations in U+U collisions at $\sqrt{s_{NN}} = 193$ GeV. This effect is more pronounced for the excited state due to its smaller binding energy and heightened sensitivity to the initial energy density of the hot QCD medium.

nucl-th↗

arXiv2026

Physics-Informed Neural Network for Solving the Heavy Quark Diffusion in the Expanding QCD Medium

We employ Physics-Informed Neural Networks (PINNs) to investigate the dynamical evolution of heavy quarks within the expanding hot QCD medium generated in relativistic heavy-ion collisions. The heavy quark dynamics are first modeled under the assumption of complete kinetic thermalization, followed by a more realistic study of non-thermal diffusion governed by the Fokker-Planck (FP) equation. In both scenarios, the background evolution of the hot QCD medium is encoded into the coefficients of the diffusion equations. These equations are solved within the PINN framework, where the initial conditions, physical constraints from the dynamical equation, and probability conservation are incorporated into the loss function.We also compare the performance of the FP-PINN with a supervised five-dimensional DNN trained on labeled data generated from Langevin-based reference distributions.This work provides a valuable reference for applying PINN-based models to particle diffusion in phase space, laying the foundation for future studies of heavy quarkonium production via realistic non-thermal heavy-quark coalescence.

nucl-th↗

arXiv2025

Triple-charmed Hadron from Coalescence in Relativistic Heavy-Ion Collisions

We investigate the production of the $Ω_{ccc}$ baryon in relativistic heavy-ion collisions. Unlike proton-proton collisions, nuclear collisions produce both deconfined matter and abundant charm quark pairs, which can coalesce to form the $Ω_{ccc}$ baryon, thereby significantly enhancing its production. We employ the Langevin model and the Instantaneous Coalescence Model (LICM), coupled with hydrodynamic simulations, to study charm quark diffusion and coalescence into the $Ω_{ccc}$ baryon in expanding QCD matter. The production of the $Ω_{ccc}$ is governed by the charm quark densities and the in-medium wavefunctions of the $Ω_{ccc}$, which determines the coalescence probability for the three charm quarks. We calculate the $Ω_{ccc}$ production with realistic charm diffusions and different in-medium wave functions of $Ω_{ccc}$ baryon. We find that the production of the $Ω_{ccc}$ baryon is sensitive to these factors, which aids in understanding its properties in the hot QCD medium.

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arXiv2025

Properties of Interstellar Medium in the S0 Galaxy NGC 1222: Evidence for Shocked-enhanced Line Emission

In this paper we present a comprehensive study on the properties of the interstellar medium in NGC~1222, a star-forming early-type merging galaxy that forms a triple system, using optical and far-infrared (FIR) spectroscopic, and multiband photometric data. The fit to the spectral energy distribution reveals a high dust content in the galaxy, with a dust-to-stellar mass ratio of $M_\mathrm{dust}/M_\star\sim3.3\times10^{-3}$ that is 40$-$90 larger than the mean value of local S0 galaxies. By comparing the observed optical emission line ratios to shock models, we suggest that a merger-induced shock, which is further supported by the higher-than-average \OI- and \CII-to-PAH ratios, plays a role in heating the gas in NGC~1222. We also show evidence for gas inflow by analysing the kinematic properties of NGC~1222.

astro-ph.GA↗

arXiv2025

A Method to Decipher "Genome" from Interatomic Cohesion in the Exploration for a "Central Dogma" Replacement in Material Science

In the ball-stick model, interatomic cohesions are considered "sticks". But enormous details and features of the "sticks" are usually oversimplified as indexed quantities or equivocated as geometry characteristics. These indexed quantities or geometry characteristics not only limit the explanatory capability to a few chemical/physical aspects but also eliminate generativity for expected resemblance. And these limitations can be related to the information loss during the conversion. Herein, inspired by the central dogma, a framework is introduced to compact interatomic cohesions into a detailed residue-by-residue "genome" with matched encoding/decoding tools. The framework fuses the quantum mechanical aspects, auto feature extraction, nanostructures and/or simulations, and generative models. As a proof of concept, the realization introduced in this work adopted bosonic/fermionic features, an autoencoder with image recognition processes, Density Functional Theory simulations, and a thiolate-protected gold nanocluster dataset. After repetitive modeling, validating, and analysis based on 26,528 simulated interatomic images, the interatomic cohesion can be almost losslessly encoded into an 8-value-genome, and the genome encoder-decoder pair is also obtained. The model is then automatically extended into a generative model which converts any arbitrary 8-value-genome to a bond image.

cond-mat.mes-hall↗

arXiv2024

Charmonia Production in Hot QCD Matter and Electromagnetic Fields

Both hot QCD matter and extremely strong electromagnetic fields are generated in relativistic heavy-ion collisions. We employ the transport model and the equivalent photon approximation (EPA) to study charmonium hadroproduction and photoproduction in nucleus-nucleus collisions, respectively. In photoproduction, quasi-real photons may interact with the whole nucleus or individual nucleons, which is called the coherent and incoherent processes, respectively. The typical momentum of charmonium produced in two processes is located in $p_T\lesssim 1/R_A$ and $p_T\lesssim 1/R_N$, where $R_A$ and $R_N$ are the radii of nucleus and the nucleon. Both kinds of photoproduction and also hadroproduction are considered to calculate charmonium production in different transverse momentum bins, rapidity bins, and collision centralities, incorporating modifications from hot QCD matter and initial cold nuclear matter effects. Our calculations explain experimental data about charmonium nuclear modification factors and the production cross-section in ultra-peripheral collisions. Charmonium nuclear modification is far above the unit at extremely low $p_T$ ($p_T < 0.1$ GeV/c) in peripheral collisions with centrality 70-90\%, attributed to coherent photoproduction.

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arXiv2024

Ionized Carbon in Galaxies: The [C II] 158 $μ$m Line as a Total Molecular Gas Mass Tracer Revisited

In this paper we present a statistical study of the [C II] 158 $μ$m line and the CO(1-0) emission for a sample of $\sim$200 local and high-$z$ (32 sources with $z>1$) galaxies with much different physical conditions. We explore the correlation between the luminosities of [C II] and CO(1-0) lines, and obtain a strong linear relationship, confirming that [C II] is able to trace total molecular gas mass, with a small difference between (U)LIRGs and less-luminous galaxies. The tight and linear relation between [C II] and CO(1-0) is likely determined by the average value of the observed visual extinction $A_V$ and the range of $G_0/n$ in galaxies. Further investigations into the dependence of $L_{\mathrm{[CII]}}/L_{\mathrm{CO(1-0)}}$ on different physical properties show that $L_{\mathrm{[CII]}}/L_{\mathrm{CO(1-0)}}$ (1) anti-correlates with $Σ_{\mathrm{IR}}$, and the correlation becomes steeper when $Σ_{\mathrm{IR}} \gtrsim 10^{11}$ $L_\odot\,\mathrm{kpc}^{-2}$; (2) correlates positively with the distance from the main sequence $Δ(\mathrm{MS})$ when $Δ(\mathrm{MS})\lesssim 0$; and (3) tends to show a systematically smaller value in systems where the [C II] emission is dominated by ionized gas. Our results imply that caution needs to be taken when applying a constant [[C II]-to-$M_{\mathrm{H_2}}$ conversion factor to estimate the molecular gas content in extreme cases, such as galaxies having low-level star formation activity or high SFR surface density.

astro-ph.GA↗

arXiv2024

Design Frameworks for Spatial Zone Agents in XRI Metaverse Smart Environments

The spatial XR-IoT (XRI) Zone Agents concept combines Extended Reality (XR), the Internet of Things (IoT), and spatial computing concepts to create hyper-connected spaces for metaverse applications; envisioning space as zones that are social, smart, scalable, expressive, and agent-based. These zone agents serve as applications and agents (partners, assistants, or guides) for users co-living and co-operating together in a shared spatial context. The zone agent concept is toward reducing the gap between the physical environment (space) and the classical two-dimensional user interface, through space-based interactions for future metaverse applications. This integration aims to enrich user engagement with their environments through intuitive and immersive experiences and pave the way for innovative human-machine interaction in smart spaces. Contributions include: i) a theoretical framework for creating XRI zone/space-agents using Mixed-Reality Agents (MiRAs) and XRI theory, ii) agent and scene design for spatial zone agents, and iii) prototype and user interaction design scenario concepts for human-to-space agent relationships in an early immersive smart-space application.

cs.HC↗

arXiv2023

Functional Group Induced Transformations in Stacking and Electron Structure in Mo2CTx/NiS Heterostructures

The two-dimensional transition metal carbide/nitride family (MXenes) has garnered significant attention due to their highly customizable surface functional groups. Leveraging modern material science techniques, the customizability of MXenes can be enhanced further through the construction of associated heterostructures. As indicated by recent research, the Mo2CTx/NiS heterostructure has emerged as a promising candidate exhibiting superior physical and chemical application potential. The geometrical structure of Mo2CTx/NiS heterostructure is modeled and 6 possible configurations are validated by Density Functional Theory simulations. The variation in functional groups leads to structural changes in Mo2CTx/NiS interfaces, primarily attributed to the competition between van der Waals and covalent interactions. The presence of different functional groups results in significant band fluctuations near the Fermi level for Ni and Mo atoms, influencing the role of atoms and electron's ability to escape near the interface. This, in turn, modulates the strength of covalent interactions at the MXenes/NiS interface and alters the ease of dissociation of the MXenes/NiS complex. Notably, the Mo2CO2/NiS(P6_3/mmc) heterostructure exhibits polymorphism, signifying that two atomic arrangements can stabilize the structure. The transition process between these polymorphs is also simulated, further indicating the modulation of the electronic level of properties by a sliding operation.

cond-mat.mes-hall↗

arXiv2023

Attentive Q-Matrix Learning for Knowledge Tracing

As the rapid development of Intelligent Tutoring Systems (ITS) in the past decade, tracing the students' knowledge state has become more and more important in order to provide individualized learning guidance. This is the main idea of Knowledge Tracing (KT), which models students' mastery of knowledge concepts (KCs, skills needed to solve a question) based on their past interactions on platforms. Plenty of KT models have been proposed and have shown remarkable performance recently. However, the majority of these models use concepts to index questions, which means the predefined skill tags for each question are required in advance to indicate the KCs needed to answer that question correctly. This makes it pretty hard to apply on large-scale online education platforms where questions are often not well-organized by skill tags. In this paper, we propose Q-matrix-based Attentive Knowledge Tracing (QAKT), an end-to-end style model that is able to apply the attentive method to scenes where no predefined skill tags are available without sacrificing its performance. With a novel hybrid embedding method based on the q-matrix and Rasch model, QAKT is capable of modeling problems hierarchically and learning the q-matrix efficiently based on students' sequences. Meanwhile, the architecture of QAKT ensures that it is friendly to questions associated with multiple skills and has outstanding interpretability. After conducting experiments on a variety of open datasets, we empirically validated that our model shows similar or even better performance than state-of-the-art KT methods. Results of further experiments suggest that the q-matrix learned by QAKT is highly model-agnostic and more information-sufficient than the one labeled by human experts, which could help with the data mining tasks in existing ITSs.

cs.CY↗

arXiv2019

ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining

Automatic lesion detection from computed tomography (CT) scans is an important task in medical imaging analysis. It is still very challenging due to similar appearances (e.g. intensity and texture) between lesions and other tissues, making it especially difficult to develop a universal lesion detector. Instead of developing a specific-type lesion detector, this work builds a Universal Lesion Detector (ULDor) based on Mask R-CNN, which is able to detect all different kinds of lesions from whole body parts. As a state-of-the-art object detector, Mask R-CNN adds a branch for predicting segmentation masks on each Region of Interest (RoI) to improve the detection performance. However, it is almost impossible to manually annotate a large-scale dataset with pixel-level lesion masks to train the Mask R-CNN for lesion detection. To address this problem, this work constructs a pseudo mask for each lesion region that can be considered as a surrogate of the real mask, based on which the Mask R-CNN is employed for lesion detection. On the other hand, this work proposes a hard negative example mining strategy to reduce the false positives for improving the detection performance. Experimental results on the NIH DeepLesion dataset demonstrate that the ULDor is enhanced using pseudo masks and the proposed hard negative example mining strategy and achieves a sensitivity of 86.21% with five false positives per image.

cs.CV↗

arXiv2016

A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling

Robust automated organ segmentation is a prerequisite for computer-aided diagnosis (CAD), quantitative imaging analysis and surgical assistance. For high-variability organs such as the pancreas, previous approaches report undesirably low accuracies. We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels. There are four stages: 1) decomposing CT slice images as a set of disjoint boundary-preserving superpixels; 2) computing pancreas class probability maps via dense patch labeling; 3) classifying superpixels by pooling both intensity and probability features to form empirical statistics in cascaded random forest frameworks; and 4) simple connectivity based post-processing. The dense image patch labeling are conducted by: efficient random forest classifier on image histogram, location and texture features; and more expensive (but with better specificity) deep convolutional neural network classification on larger image windows (with more spatial contexts). Evaluation of the approach is performed on a database of 80 manually segmented CT volumes in six-fold cross-validation (CV). Our achieved results are comparable, or better than the state-of-the-art methods (evaluated by "leave-one-patient-out"), with Dice 70.7% and Jaccard 57.9%. The computational efficiency has been drastically improved in the order of 6~8 minutes, comparing with others of ~10 hours per case. Finally, we implement a multi-atlas label fusion (MALF) approach for pancreas segmentation using the same datasets. Under six-fold CV, our bottom-up segmentation method significantly outperforms its MALF counterpart: (70.7 +/- 13.0%) versus (52.5 +/- 20.8%) in Dice. Deep CNN patch labeling confidences offer more numerical stability, reflected by smaller standard deviations.

cs.CV↗

arXiv2015

Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation

Automated computer-aided detection (CADe) in medical imaging has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities but at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-to-fine cascade framework that first operates a candidate generation system at sensitivities of $\sim$100% but at high FP levels. By leveraging existing CAD systems, coordinates of regions or volumes of interest (ROI or VOI) for lesion candidates are generated in this step and function as input for a second tier, which is our focus in this study. In this second stage, we generate $N$ 2D (two-dimensional) or 2.5D views via sampling through scale transformations, random translations and rotations with respect to each ROI's centroid coordinates. These random views are used to train deep convolutional neural network (ConvNet) classifiers. In testing, the trained ConvNets are employed to assign class (e.g., lesion, pathology) probabilities for a new set of $N$ random views that are then averaged at each ROI to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. The methods are evaluated on three different data sets with different numbers of patients: 59 patients for sclerotic metastases detection, 176 patients for lymph node detection, and 1,186 patients for colonic polyp detection. Experimental results show the ability of ConvNets to generalize well to different medical imaging CADe applications and scale elegantly to various data sets. Our proposed methods improve CADe performance markedly in all cases. CADe sensitivities improved from 57% to 70%, from 43% to 77% and from 58% to 75% at 3 FPs per patient for sclerotic metastases, lymph nodes and colonic polyps, respectively.

cs.CV↗

arXiv2015

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhibits previous segmentation methods from achieving high accuracies, especially compared to other organs such as the liver, heart or kidneys. In this paper, we present a probabilistic bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans, using multi-level deep convolutional networks (ConvNets). We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i.e. superpixels. We first present a dense labeling of local image patches via $P{-}\mathrm{ConvNet}$ and nearest neighbor fusion. Then we describe a regional ConvNet ($R_1{-}\mathrm{ConvNet}$) that samples a set of bounding boxes around each image superpixel at different scales of contexts in a "zoom-out" fashion. Our ConvNets learn to assign class probabilities for each superpixel region of being pancreas. Last, we study a stacked $R_2{-}\mathrm{ConvNet}$ leveraging the joint space of CT intensities and the $P{-}\mathrm{ConvNet}$ dense probability maps. Both 3D Gaussian smoothing and 2D conditional random fields are exploited as structured predictions for post-processing. We evaluate on CT images of 82 patients in 4-fold cross-validation. We achieve a Dice Similarity Coefficient of 83.6$\pm$6.3% in training and 71.8$\pm$10.7% in testing.

cs.CV↗