arXiv subjects

Yu Rong

Publications and source records attributed to Yu Rong.

At least 91 records · Page 5Linked to original sources

arXiv2025

MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra

Establishing the relationship between 3D structures and the energy states of molecular systems has proven to be a promising approach for learning 3D molecular representations. However, existing methods are limited to modeling the molecular energy states from classical mechanics. This limitation results in a significant oversight of quantum mechanical effects, such as quantized (discrete) energy level structures, which offer a more accurate estimation of molecular energy and can be experimentally measured through energy spectra. In this paper, we propose to utilize the energy spectra to enhance the pre-training of 3D molecular representations (MolSpectra), thereby infusing the knowledge of quantum mechanics into the molecular representations. Specifically, we propose SpecFormer, a multi-spectrum encoder for encoding molecular spectra via masked patch reconstruction. By further aligning outputs from the 3D encoder and spectrum encoder using a contrastive objective, we enhance the 3D encoder's understanding of molecules. Evaluations on public benchmarks reveal that our pre-trained representations surpass existing methods in predicting molecular properties and modeling dynamics.

cs.LG↗

arXiv2025

Large Language-Geometry Model: When LLM meets Equivariance

Accurately predicting 3D structures and dynamics of physical systems is crucial in scientific applications. Existing approaches that rely on geometric Graph Neural Networks (GNNs) effectively enforce $\mathrm{E}(3)$-equivariance, but they often fall in leveraging extensive broader information. While direct application of Large Language Models (LLMs) can incorporate external knowledge, they lack the capability for spatial reasoning with guaranteed equivariance. In this paper, we propose EquiLLM, a novel framework for representing 3D physical systems that seamlessly integrates E(3)-equivariance with LLM capabilities. Specifically, EquiLLM comprises four key components: geometry-aware prompting, an equivariant encoder, an LLM, and an equivariant adaptor. Essentially, the LLM guided by the instructive prompt serves as a sophisticated invariant feature processor, while 3D directional information is exclusively handled by the equivariant encoder and adaptor modules. Experimental results demonstrate that EquiLLM delivers significant improvements over previous methods across molecular dynamics simulation, human motion simulation, and antibody design, highlighting its promising generalizability.

cs.LG↗

arXiv2025

Diffusion Models for Molecules: A Survey of Methods and Tasks

Generative tasks about molecules, including but not limited to molecule generation, are crucial for drug discovery and material design, and have consistently attracted significant attention. In recent years, diffusion models have emerged as an impressive class of deep generative models, sparking extensive research and leading to numerous studies on their application to molecular generative tasks. Despite the proliferation of related work, there remains a notable lack of up-to-date and systematic surveys in this area. Particularly, due to the diversity of diffusion model formulations, molecular data modalities, and generative task types, the research landscape is challenging to navigate, hindering understanding and limiting the area's growth. To address this, this paper conducts a comprehensive survey of diffusion model-based molecular generative methods. We systematically review the research from the perspectives of methodological formulations, data modalities, and task types, offering a novel taxonomy. This survey aims to facilitate understanding and further flourishing development in this area. The relevant papers are summarized at: https://github.com/AzureLeon1/awesome-molecular-diffusion-models.

cs.LG↗

arXiv2025

Can Large Language Models Be Query Optimizer for Relational Databases?

Query optimization, which finds the optimized execution plan for a given query, is a complex planning and decision-making problem within the exponentially growing plan space in database management systems (DBMS). Traditional optimizers heavily rely on a certain cost model constructed by various heuristics and empirical tuning, probably leading to generating suboptimal plans. Recent developments of Large Language Models (LLMs) have demonstrated their potential in solving complex planning and decision-making problems, such as arithmetic and programmatic tasks. In this paper, we try to explore the potential of LLMs in handling query optimization and propose a tentative LLM-based query optimizer dubbed LLM-QO, established on PostgreSQL's execution engine. In LLM-QO, we formulate query optimization in an autoregressive fashion which directly generates the execution plan without explicit plan enumeration. To investigate the essential input of LLM-QO, we design a customized data recipe named QInstruct to collect the training data from various optimizers and serialize the database's meta data, queries and corresponding plans into a textual format. Based on QInstruct, we implement a two-stage fine-tuning pipeline, Query Instruction Tuning (QIT) and Query Direct Preference Optimization (QDPO), to empower the capability of general-purpose LLMs in handling query optimization. In our experiments, LLM-QO can generate valid and high-quality plans and consistently outperforms both traditional and learned optimizers on three query workloads. Our findings verify that LLMs can be derived as query optimizers where generalization, efficiency and adaptivity deserve further research efforts.

cs.DB↗

arXiv2025

Natural Language-Assisted Multi-modal Medication Recommendation

Combinatorial medication recommendation(CMR) is a fundamental task of healthcare, which offers opportunities for clinical physicians to provide more precise prescriptions for patients with intricate health conditions, particularly in the scenarios of long-term medical care. Previous research efforts have sought to extract meaningful information from electronic health records (EHRs) to facilitate combinatorial medication recommendations. Existing learning-based approaches further consider the chemical structures of medications, but ignore the textual medication descriptions in which the functionalities are clearly described. Furthermore, the textual knowledge derived from the EHRs of patients remains largely underutilized. To address these issues, we introduce the Natural Language-Assisted Multi-modal Medication Recommendation(NLA-MMR), a multi-modal alignment framework designed to learn knowledge from the patient view and medication view jointly. Specifically, NLA-MMR formulates CMR as an alignment problem from patient and medication modalities. In this vein, we employ pretrained language models(PLMs) to extract in-domain knowledge regarding patients and medications, serving as the foundational representation for both modalities. In the medication modality, we exploit both chemical structures and textual descriptions to create medication representations. In the patient modality, we generate the patient representations based on textual descriptions of diagnosis, procedure, and symptom. Extensive experiments conducted on three publicly accessible datasets demonstrate that NLA-MMR achieves new state-of-the-art performance, with a notable average improvement of 4.72% in Jaccard score. Our source code is publicly available on https://github.com/jtan1102/NLA-MMR_CIKM_2024.

cs.AI↗

arXiv2024

Ultra Diffuse Dwarf Galaxies Hosting Pseudo-bulges

By analyzing data from DESI Legacy Imaging Survey of the dwarf galaxies in the Arecibo Legacy Fast Alfa Survey, we have identified five ultra-diffuse galaxies (UDGs) featuring central pseudo-bulges. These UDGs display blue pseudo-bulges with Sérsic indices $n<2.5$ and effective radii spanning 300-700 pc, along with bluer thin stellar disks exhibiting low surface brightness and expansive effective radii that align with the UDG definition. The rotation velocities of these UDGs, determined using HI line widths and optical inclinations, exceed those of most dwarf galaxies of similar mass, suggesting the high halo spins or substantial dark matter halos. We propose that these UDGs likely formed through mergers of dwarf galaxies lacking old stars in their progenitors, resulting in the development of central bulge-like structures during starbursts triggered by the mergers, while also enhancing their halo spin. Subsequent gas accretion facilitated the formation of extended stellar disks. It is also worth noting the possibility that these UDGs could alternatively represent ``failed $L^{\star}$ galaxies'' with massive dark matter halos but reduced star formation efficiencies. If future high-resolution HI observations confirm the presence of massive halos around these UDGs, they may have formed due to intense AGN feedback in the early universe, and may be the descendants of ``little red dots'' observed by the James Webb Space Telescope, which are characterized by heightened central black hole masses and intensified accretion and feedback processes in the early universe.

astro-ph.GA↗

arXiv2024

Annotation-guided Protein Design with Multi-Level Domain Alignment

The core challenge of de novo protein design lies in creating proteins with specific functions or properties, guided by certain conditions. Current models explore to generate protein using structural and evolutionary guidance, which only provide indirect conditions concerning functions and properties. However, textual annotations of proteins, especially the annotations for protein domains, which directly describe the protein's high-level functionalities, properties, and their correlation with target amino acid sequences, remain unexplored in the context of protein design tasks. In this paper, we propose Protein-Annotation Alignment Generation, PAAG, a multi-modality protein design framework that integrates the textual annotations extracted from protein database for controllable generation in sequence space. Specifically, within a multi-level alignment module, PAAG can explicitly generate proteins containing specific domains conditioned on the corresponding domain annotations, and can even design novel proteins with flexible combinations of different kinds of annotations. Our experimental results underscore the superiority of the aligned protein representations from PAAG over 7 prediction tasks. Furthermore, PAAG demonstrates a significant increase in generation success rate (24.7% vs 4.7% in zinc finger, and 54.3% vs 22.0% in the immunoglobulin domain) in comparison to the existing model. We anticipate that PAAG will broaden the horizons of protein design by leveraging the knowledge from between textual annotation and proteins.

q-bio.QM↗

arXiv2024

All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction

Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations among existing data, such as collaborative filtering, social recommendations and heterogeneous information network, to alleviate the data insufficiency issue for cold-start users and items. However, the explicit relations constructed based on data between different roles may be unreliable and irrelevant, which limits the performance ceiling of the specific recommendation task. Motivated by this, in this paper, we propose a flexible framework dubbed heterogeneous interaction rating network (HIRE). HIRE dose not solely rely on the pre-defined interaction pattern or the manually constructed heterogeneous information network. Instead, we devise a Heterogeneous Interaction Module (HIM) to jointly model the heterogeneous interactions and directly infer the important interactions via the observed data. In the experiments, we evaluate our model under three cold-start settings on three real-world datasets. The experimental results show that HIRE outperforms other baselines by a large margin. Furthermore, we visualize the inferred interactions of HIRE to confirm the contribution of our model.

cs.IR↗

arXiv2024

Moderate Influence of Halo Spin on Stellar Mass Distributions in Dwarf and Massive Galaxies

We estimate halo spins for HI-rich galaxies in the Arecibo Legacy Fast Alfa Survey using a semi-analytic approach, examining the relationship between halo spin and stellar surface density. Our findings reveal an inverse correlation in both low- and high-mass galaxy samples, with stellar surface density decreasing as halo spin increases. This trend highlights the pivotal role of halo spin in galaxy evolution and suggests a universal formation scenario: high-spin halos, accompanied by high-spin accreted gas, retain angular momentum, preventing gas from efficiently condensing in the galactic center and thus suppressing star formation. Consequently, weak feedback redistributes gas to the halo outskirts without significant expulsion. The shallower central gravitational potential in high-spin halos promotes outward stellar migration, leading to more extended stellar distributions and lower stellar surface densities.

astro-ph.GA↗

arXiv2024

Size Growth on Short Timescales of Star-Forming Galaxies: Insights from Size Variation with Rest-Frame Wavelength with JADES

We investigate size variation with rest-frame wavelength for star-forming galaxies based on the second JWST Advanced Deep Extragalactic Survey data release. Star-forming galaxies are typically smaller at longer wavelength from UV-to-NIR at $z<3.5$, especially for more massive galaxies, indicating the inside-out assembly with in-situ star formation if ignoring dust attenuation. The size variation with wavelength shows strong dependence on stellar mass, and shows little or no dependence on redshift, specific star formation rate and galaxy environment. This suggests that the size growth of star-forming galaxies is a self-regulated process primarily governed by stellar mass. We model size as a function of both mass and redshift simultaneously, obtaining $R_{\rm e} \propto M_*^{0.23} (1+z)^{-1.04}$ at a wavelength of 0.45 ${μ\mathrm{m}}$, and $R_{\rm e} \propto M_*^{0.20} (1+z)^{-1.08}$ at 1.0 ${μ\mathrm{m}}$. Based on this size evolution and the star formation main sequence from the literature, we obtain the locus of typical size growth for individual galaxies of different masses on the mass-size plane. The moving trend of galaxies on the mass-size plane, which indicates the slopes of their locus, strongly correlates with the size ratio between 0.45 ${μ\mathrm{m}}$ and 1.0 ${μ\mathrm{m}}$, supporting the idea that the size variation with wavelength provides important information on size growth of galaxies on short timescales.

astro-ph.GA↗

arXiv2024

Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents

Effective research ideation is a critical step for scientific research. However, the exponential increase in scientific literature makes it challenging for researchers to stay current with recent advances and identify meaningful research directions. Recent developments in large language models~(LLMs) suggest a promising avenue for automating the generation of novel research ideas. However, existing methods for idea generation either trivially prompt LLMs or directly expose LLMs to extensive literature without indicating useful information. Inspired by the research process of human researchers, we propose a Chain-of-Ideas~(CoI) agent, an LLM-based agent that organizes relevant literature in a chain structure to effectively mirror the progressive development in a research domain. This organization facilitates LLMs to capture the current advancements in research, thereby enhancing their ideation capabilities. Furthermore, we propose Idea Arena, an evaluation protocol that can comprehensively evaluate idea generation methods from different perspectives, aligning closely with the preferences of human researchers. Experimental results indicate that the CoI agent consistently outperforms other methods and shows comparable quality as humans in research idea generation. Moreover, our CoI agent is budget-friendly, with a minimum cost of \$0.50 to generate a candidate idea and its corresponding experimental design.

cs.AI↗

arXiv2024

Graph Pre-Training Models Are Strong Anomaly Detectors

Graph Anomaly Detection (GAD) is a challenging and practical research topic where Graph Neural Networks (GNNs) have recently shown promising results. The effectiveness of existing GNNs in GAD has been mainly attributed to the simultaneous learning of node representations and the classifier in an end-to-end manner. Meanwhile, graph pre-training, the two-stage learning paradigm such as DGI and GraphMAE, has shown potential in leveraging unlabeled graph data to enhance downstream tasks, yet its impact on GAD remains under-explored. In this work, we show that graph pre-training models are strong graph anomaly detectors. Specifically, we demonstrate that pre-training is highly competitive, markedly outperforming the state-of-the-art end-to-end training models when faced with limited supervision. To understand this phenomenon, we further uncover pre-training enhances the detection of distant, under-represented, unlabeled anomalies that go beyond 2-hop neighborhoods of known anomalies, shedding light on its superior performance against end-to-end models. Moreover, we extend our examination to the potential of pre-training in graph-level anomaly detection. We envision this work to stimulate a re-evaluation of pre-training's role in GAD and offer valuable insights for future research.

cs.LG↗

arXiv2024

Intrinsic Morphology of The Stellar Components in HI-bearing Dwarf Galaxies and The Dependence on Mass

The intrinsic morphology of stellar components within HI-bearing dwarf galaxies remains a topic of uncertainty. Leveraging the galaxy dataset derived from the cross-matched catalog of the Arecibo Legacy Fast Arecibo L-band Feed Array HI 21cm line survey and the Sloan Digital Sky Survey, we employ a Markov Chain Monte Carlo methodology and assume a triaxial model to scrutinize the inherent stellar distributions of these HI-bearing dwarf galaxies. Our analysis indicates a preference for oblate-triaxial models with $C<B\lesssim A$, indicative of thick stellar disks, characterizing the stellar components in these HI-bearing dwarfs with stellar masses ranging between $10^7\--10^{9.5}\ M_{\odot}$. The average thickness of the stellar components in HI-bearing dwarf galaxies approximates $C/A\sim 0.4$. Furthermore, we observe that the thickness of the stellar disks exhibits weak or negligible dependence on the stellar masses of HI-bearing galaxies.

astro-ph.GA↗

arXiv2024

Bipolar blobs as evidence of hidden AGN activities in the low-mass galaxies

We report the evidence of a hidden black hole (BH) in a low-mass galaxy, MaNGA 9885-9102, and provide a new method to identify active BH in low mass galaxies. This galaxy is originally selected from the MaNGA survey with distinctive bipolar H$α$ blobs at the minor axis. The bipolar feature can be associated with AGN activity, while the two blobs are classified as the H II regions on the BPT diagram, making the origins confusing. The Swift UV continuum shows that the two blobs do not have UV counterparts, suggesting that the source of ionization is out of the blobs. Consistent with this, the detailed photoionization models prefer to AGN rather than star-forming origin with a significance of 5.8$σ$. The estimated BH mass is $M_{\rm BH}\sim$7.2$\times 10^5 M_\odot$ from the $M_{\rm BH}-σ_*$ relationship. This work introduces a novel method for detecting the light echo of BHs, potentially extending to intermediate mass, in low metallicity environments where the traditional BPT diagram fails.

astro-ph.GA↗

arXiv2024

GenCA: A Text-conditioned Generative Model for Realistic and Drivable Codec Avatars

Photo-realistic and controllable 3D avatars are crucial for various applications such as virtual and mixed reality (VR/MR), telepresence, gaming, and film production. Traditional methods for avatar creation often involve time-consuming scanning and reconstruction processes for each avatar, which limits their scalability. Furthermore, these methods do not offer the flexibility to sample new identities or modify existing ones. On the other hand, by learning a strong prior from data, generative models provide a promising alternative to traditional reconstruction methods, easing the time constraints for both data capture and processing. Additionally, generative methods enable downstream applications beyond reconstruction, such as editing and stylization. Nonetheless, the research on generative 3D avatars is still in its infancy, and therefore current methods still have limitations such as creating static avatars, lacking photo-realism, having incomplete facial details, or having limited drivability. To address this, we propose a text-conditioned generative model that can generate photo-realistic facial avatars of diverse identities, with more complete details like hair, eyes and mouth interior, and which can be driven through a powerful non-parametric latent expression space. Specifically, we integrate the generative and editing capabilities of latent diffusion models with a strong prior model for avatar expression driving. Our model can generate and control high-fidelity avatars, even those out-of-distribution. We also highlight its potential for downstream applications, including avatar editing and single-shot avatar reconstruction.

cs.CV↗

arXiv2024

Exploring the origin of cold gas and star formation in a rare population of strongly bulge-dominated early-type Galaxies

We analyze the properties of a rare population, the strongly bulge-dominated early-type galaxies (referred to as sBDEs) with significant HI gas, using the databases from the FAST All Sky HI survey (FASHI) and the Arecibo Legacy Fast ALFA (ALFALFA) survey. We select the sBDEs from the Sloan Digital Sky Survey (SDSS) and cross-match with the FASHI-ALFALFA combined HI sample, resulting in 104 HI-rich sBDEs. These sBDEs tend to have extremely high HI reservoirs, which is rare in previous studies such as ATLAS$^{3D}$. 70% of the selected sBDEs are classified as quiescent galaxies, even though they have a large HI reservoir. We study the properties of these sBDEs from five main aspects: stellar population, gas-phase metallicity, stacked HI spectra, environment, and spatially resolved MaNGA data. The majority of HI-rich sBDEs appear to show lower gas-phase metallicity and are located in significantly lower-density environments, suggesting an external origin for their HI gas. We find that star-forming sBDEs exhibit statistically higher star formation efficiency and slightly older stellar populations compared to normal star-forming galaxies, suggesting a recent star formation on Gyr-timescale. They also show narrower and more concentrated HI profiles compared to control star-forming galaxies, which may explain their higher star formation efficiency.

astro-ph.GA↗

arXiv2024

Galaxy Group Ellipticity Confirms a Younger Cosmos

We present an analysis of the ellipticities of galaxy groups, derived from the spatial distribution of member galaxies, revealing a notable incongruity between the observed local galaxy groups and their counterparts in the Lambda cold dark matter cosmology. Specifically, our investigation reveals a substantial disparity in the ellipticities of observed groups with masses \mbox{$10^{13.0}<M_{\rm{h}}<10^{14.5}\ {\rm M_{\odot}}\ h^{-1}$} exhibiting significantly higher ellipticities (at a confidence level of approximately $4σ$) compared to their simulated counterparts. Notably, the consistent use of the same group finder for identifying galaxy groups in both observational and simulated datasets underscores the robustness of this result. This observation may imply a potential incongruence between the inferred age of the Universe from observations and the predictions of the model, which aligns with the younger Universe hypothesis suggested by the elevated fraction of observed satellite pairs with correlated line-of-sight relative velocities compared to simulations. Our findings significantly strengthen the plausibility of a younger age for our Universe.

astro-ph.GA↗

arXiv2024

Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning

Incorporating Euclidean symmetries (e.g. rotation equivariance) as inductive biases into graph neural networks has improved their generalization ability and data efficiency in unbounded physical dynamics modeling. However, in various scientific and engineering applications, the symmetries of dynamics are frequently discrete due to the boundary conditions. Thus, existing GNNs either overlook necessary symmetry, resulting in suboptimal representation ability, or impose excessive equivariance, which fails to generalize to unobserved symmetric dynamics. In this work, we propose a general Discrete Equivariant Graph Neural Network (DEGNN) that guarantees equivariance to a given discrete point group. Specifically, we show that such discrete equivariant message passing could be constructed by transforming geometric features into permutation-invariant embeddings. Through relaxing continuous equivariant constraints, DEGNN can employ more geometric feature combinations to approximate unobserved physical object interaction functions. Two implementation approaches of DEGNN are proposed based on ranking or pooling permutation-invariant functions. We apply DEGNN to various physical dynamics, ranging from particle, molecular, crowd to vehicle dynamics. In twenty scenarios, DEGNN significantly outperforms existing state-of-the-art approaches. Moreover, we show that DEGNN is data efficient, learning with less data, and can generalize across scenarios such as unobserved orientation.

cs.LG↗