arXiv subjects

Long Wang

Publications and source records attributed to Long Wang.

At least 73 records · Page 4Linked to original sources

arXiv2025

Data Dependency-Aware Code Generation from Enhanced UML Sequence Diagrams

Large language models (LLMs) excel at generating code from natural language (NL) descriptions. However, the plain textual descriptions are inherently ambiguous and often fail to capture complex requirements like intricate system behaviors, conditional logic, and architectural constraints; implicit data dependencies in service-oriented architectures are difficult to infer and handle correctly. To bridge this gap, we propose a novel step-by-step code generation framework named UML2Dep by leveraging unambiguous formal specifications of complex requirements. First, we introduce an enhanced Unified Modeling Language (UML) sequence diagram tailored for service-oriented architectures. This diagram extends traditional visual syntax by integrating decision tables and API specifications, explicitly formalizing structural relationships and business logic flows in service interactions to rigorously eliminate linguistic ambiguity. Second, recognizing the critical role of data flow, we introduce a dedicated data dependency inference (DDI) task. DDI systematically constructs an explicit data dependency graph prior to actual code synthesis. To ensure reliability, we formalize DDI as a constrained mathematical reasoning task through novel prompting strategies, aligning with LLMs' excellent mathematical strengths. Additional static parsing and dependency pruning further reduce context complexity and cognitive load associated with intricate specifications, thereby enhancing reasoning accuracy and efficiency.

cs.AI↗

arXiv2025

Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL's potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects. Code: https://github.com/muleina/CMS\_HCAL\_ML\_OnlineDQM .

cs.LG↗

arXiv2025

On the Formation of GW231123 in Population III Star Clusters

GW231123 is a binary black hole merger whose primary component lies within or above the pair-instability mass gap, while the secondary component falls within this gap. The standard theory of stellar evolution is significantly challenged by this event. We investigate the formation of candidate progenitors of GW231123 in Population III (Pop III) star clusters. We find that they could form through stellar mergers, binary black hole mergers, and mixed mergers. The mass distribution of these candidate progenitors covers the component masses of GW231123. Under our model assumptions, their predicted merger rate density spans the range of $0.001-0.26{\rm Gpc^{-3}yr^{-1}}$, encompassing that of GW231123. These findings suggest that GW231123 may originate from Pop III star clusters. Furthermore, such candidate progenitors are expected to be detectable by future gravitational wave detectors LISA/Taiji/TianQin/DECIGO/Cosmic Explorer/Einstein Telescope, which would provide valuable insights into the formation scenarios of events like GW231123.

astro-ph.GA↗

arXiv2025

Evolution of social behaviors in noisy environments

Evolutionary game theory offers a general framework to study how behaviors evolve by social learning in a population. This body of theory can accommodate a range of social dilemmas, or games, as well as real-world complexities such as spatial structure or behaviors conditioned on reputations. Nonetheless, this approach typically assumes a deterministic payoff structure for social interactions. Here, we extend evolutionary game theory to account for random changes in the social environment, so that mutual cooperation may bring different rewards today than it brings tomorrow, for example. Even when such environmental noise is unbiased, we find it can have a qualitative impact on the behaviors that evolve in a population. Noisy payoffs can permit the stable co-existence of cooperators and defectors in the prisoner's dilemma, for example, as well as bistability in snowdrift games and stable limit cycles in rock-paper-scissors games -- dynamical phenomena that cannot occur in the absence of noise. We conclude by discussing the relevance of our framework to scenarios where the nature of social interactions is subject to external perturbations.

physics.soc-ph↗

arXiv2025

DRAGON-III simulation: modelling million-body globular and nuclear star clusters

As a continuation of DRAGON-II, we present the DRAGON-III project, which focuses on the simulations of million-body globular clusters and nuclear clusters over 10 Gyr. We report on its preliminary results on globular clusters. The first 100 Myr of the simulations have produced 41 pulsars, 191 X-ray binaries, 17 gravitational wave sources, and one black hole-black hole merger due to the loss of orbital energy in the form of gravitational wave emission. The inclusion of initial soft binaries brings surprisingly interesting results, including one IMBH in a binary black hole, and compact object binaries resembling the Gaia-BH1 and the wide black hole-giant binary reported in Wang et al. (2024, Nat. Astro.).

astro-ph.GA↗

arXiv2025

Evolutionary hypergame dynamics: Introspection reasoning and social learning

In the realm of evolutionary game theory, standard frameworks typically presuppose that every player possesses comprehensive knowledge and unrestricted access to the entire strategy space. However, real-world human society inherently harbors diverse levels of knowledge, experience, and background among individuals. Hypergames incorporate this heterogeneity by permitting individuals to differ in their access to the full strategy set, reflecting cognitive or informational constraints and giving rise to asymmetric strategic interactions. Yet, their evolutionary consequences remain underexplored. Our inquiry employs prototype models featuring three available strategies, focusing on social dilemmas involving cooperation, defection, and loner. These strategies manifest cyclic dominance, akin to the well-studied rock-paper-scissors dynamics, a foundational model in game theory. Our study spans both well-mixed and spatial lattice populations, delving into the intricacies of learning and evolution of the strategy set within the evolutionary hypergame dynamics. In stark contrast to traditional evolutionary game dynamics, our findings unveil nuanced and intricate phases, encompassing scenarios of loner dominance, coexistence of multiple strategy sets, combinations of cooperation and loner dominance, and more. Remarkably, we discern that heightened rationality significantly promotes cooperative behaviors.

cs.GT↗

arXiv2025

Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system. Code: https://github.com/muleina/CMS_HCAL_ML_OnlineDQM .

cs.LG↗

arXiv2025

Star-Black Hole Interactions in Young Star Clusters

Close encounters between stars and black holes can trigger micro-tidal disruption events (micro-TDEs) in dense young star clusters (YSCs). Using direct N-body simulations with PETAR, we found that most micro-TDEs arise from few-body multiple encounters. The inferred rate is approximately 350-450 Gpc$^{-3}$ yr$^{-1}$. Micro-TDEs could be detected both by upcoming surveys such as LSST, expected to observe roughly 10-100 events per year, and by their gravitational-wave (GW) signals peaking in the deci-Hertz band, detectable with future instruments such as LGWA and DECIGO.

astro-ph.HE↗

arXiv2025

Evolutionary dynamics of memory-based strategies in repeated and structured social interactions

Human social life is shaped by repeated interactions, where past experiences guide future behavior. In evolutionary game theory, a key challenge is to identify strategies that harness such memory to succeed in repeated encounters. Decades of research have identified influential one-step memory strategies (such as Tit-for-Tat, Generous Tit-for-Tat, and Win-Stay Lose-Shift) that promote cooperation in iterated pairwise games. However, these strategies occupy only a small corner of the vast strategy space, and performance in isolated pairwise contests does not guarantee evolutionary success. The most effective strategies are those that can spread through a population and stabilize cooperation. We propose a general framework for repeated-interaction strategies that encompasses arbitrary memory lengths, diverse informational inputs (including both one's own and the opponent's past actions), and deterministic or stochastic decision rules. We analyze their evolutionary dynamics and derive general mathematical results for the emergence of cooperation in any network structure. We then introduce a unifying indicator that quantifies the contribution of repeated-interaction strategies to population-level cooperation. Applying this indicator, we show that long-memory strategies evolve to promote cooperation more effectively than short-memory strategies, challenging the traditional view that extended memory offers no advantage. This work expands the study of repeated interactions beyond one-step memory strategies to the full spectrum of memory capacities. It provides a plausible explanation for the high levels of cooperation observed in human societies, which traditional one-step memory models cannot account for.

q-bio.PE↗

arXiv2025

Cosmic Threads: Interlinking the Stellar Initial Mass Function from Star-Birth to Galaxies

The stellar initial mass function (sIMF) describes the distribution of stellar masses formed in a single star formation event in a molecular cloud clump. It is fundamental to astrophysics and cosmology, shaping our understanding of unresolved stellar populations, galactic chemical enrichment and habitable zones, and black hole growth. This White Paper reviews studies on the core mass function, stellar multiplicity, and dynamical processes affecting sIMF determinations, as well as the link between star-forming clumps and the galaxy-wide IMF (gIMF). The evidence gleaned from observed systems for the dependency of the sIMF on the metallicity and density of the clump is portrayed. We examine evidence from gravitational lensing, stellar and gas kinematics, and spectral diagnostics to assess environmental dependencies of the gIMF. Theoretical perspectives provide further insights into the sIMF's variability. Beyond summarizing current knowledge, this work aims to establish a shared framework and define strategies for studying a variable IMF in the era of near-infrared integral-field spectroscopy, 30m-class telescopes and major space-based observatories.

astro-ph.GA↗

arXiv2025

Micro-Tidal Disruption Events in Young Star Clusters

Dense young star clusters (YSCs) are ideal environments for dynamical interactions between stars and stellar mass black holes (BHs). In such dense environments, stars can undergo close encounters with BHs and fall within their tidal radius, resulting in micro-tidal disruption events (micro-TDEs), transient phenomena with potential multi-messenger signatures. We performed a suite of direct N-body simulations using the PETAR code, to which we implemented new prescriptions for modeling micro-TDEs. We constructed a set of realistic YSC models including primordial binaries, based on the observed Milky Way population. Our simulations incorporate stellar and binary evolution, supernova kicks, and stellar winds using the BSE code, and account for the Galactic tidal field via the GALPY library. We identify three dynamical channels for micro-TDE production: single star-single BH encounters, binary-mediated interactions (including supernova-kick triggers), and interactions involving higher-order multiple systems such as hierarchical triples and quadruples, as well as chaotic few-body interactions. Multiple encounters are the most efficient production channel, which dominates the total rate: 350-450 Gpc$^{-3}$ yr$^{-1}$. Micro-TDEs from YSCs are expected to be detectable by upcoming surveys, particularly the Legacy Survey of Space and Time, with detection rates potentially up to hundreds per year. The gravitational wave (GW) signals expected from micro-TDEs peak in the deci-Hertz band, making them accessible to future instruments such as the Lunar Gravitational Wave Antenna and the Deci-Hertz Interferometer Gravitational-wave Observatory. Micro-TDEs emerge as promising multi-messenger sources, potentially offering unique insights into star cluster dynamics, stellar collisions, and the population of dormant stellar-mass BHs, through both electromagnetic and GW observations.

astro-ph.HE↗

arXiv2025

Teleoperation of Continuum Instruments: Task-Priority Analysis of Linear Angular Command Interplay

This paper addresses the challenge of teleoperating continuum instruments for minimally invasive surgery (MIS). We develop and adopt a novel task-priority-based kinematic formulation to quantitatively investigate teleoperation commands for continuum instruments under remote center of motion (RCM) constraints. Using redundancy resolution methods, we investigate the kinematic performance during teleoperation, comparing linear and angular commands within a task-priority scheme. For experimental validation, an instrument module (IM) was designed and integrated with a 7-DoF manipulator. Assessments, simulations, and experimental validations demonstrated the effectiveness of the proposed framework. The experiments involved several tasks: trajectory tracking of the IM tip along multiple paths with varying priorities for linear and angular teleoperation commands, pushing a ball along predefined paths on a silicon board, following a pattern on a pegboard, and guiding the continuum tip through rings on a ring board using a standard surgical kit.

cs.RO↗

arXiv2025

Double Compact Binary Merger Rate Density in Open Star Clusters: Black Holes, Neutron Stars, and White Dwarfs

Studying compact-object binary mergers in star clusters is crucial for understanding stellar evolution and dynamical interactions in galaxies. Open clusters in particular are more abundant over cosmic time than globular clusters, however, previous research on low-mass clusters with $\lesssim 10^3~\textrm{M}_{\odot}$ has focused on binary black holes (BBHs) or black hole-neutron star (BH-NS) binaries. Binary mergers of other compact objects, such as white dwarfs (WDs), are also crucial as progenitors of transient phenomena such as Type Ia supernovae and Fast Radio Bursts. We present simulations of three types of open clusters with masses of $10^2$, $10^3$, and $10^4~\mathrm{M}_{\odot}$. In massive clusters with $\gtrsim 10^4~\textrm{M}_{\odot}$, BBHs are dynamically formed, however, less massive compact binaries such as WD-WD and WD-NS are perturbed inside the star clusters, causing them to evolve into other objects. We further find BH-NS mergers only in $10^3~\textrm{M}_{\odot}$ clusters. Considering star clusters with a typical open cluster mass, we observe that WD-WD merger rates slightly increase for $10^3~\textrm{M}_{\odot}$ clusters but decrease for $10^2~\textrm{M}_{\odot}$ clusters. Since the host clusters are tidally disrupted, most of them merge outside of the clusters. Our WD-WD merger results have further implications for two classes of transients. Super-Chandrasekhar WD-WD mergers are present in our simulations, demonstrating potential sources of Fast Radio Bursts at a rate of 70-780 Gpc$^{-3}$yr$^{-1}$, higher than the rate estimated for globular clusters. Additionally, we find that Carbon-Oxygen WD-WD mergers in our open clusters (34-640 $\textrm{Gpc}^{-3}$yr$^{-1}$) only account for 0.14-2.6% of the observed Type Ia supernova rate in our local Universe.

astro-ph.SR↗

arXiv2025

Evolutionary dynamics under coordinated reciprocity

Using past behaviors to guide future actions is essential for fostering cooperation in repeated social dilemmas. Traditional memory-based strategies that focus on recent interactions have yielded valuable insights into the evolution of cooperative behavior. However, as memory length increases, the complexity of analysis grows exponentially, since these strategies need to map every possible action sequence of a given length to subsequent responses. Due to their inherent reliance on exhaustive mapping and a lack of explicit information processing, it remains unclear how individuals can handle extensive interaction histories to make decisions under cognitive constraints. To fill this gap, we introduce coordinated reciprocity strategies ($CORE$), which incrementally evaluate the entire game history by tallying instances of consistent actions between individuals without storing round-to-round details. Once this consistency index surpasses a threshold, $CORE$ prescribes cooperation. Through equilibrium analysis, we derive an analytical condition under which $CORE$ constitutes an equilibrium. Moreover, our numerical results show that $CORE$ effectively promotes cooperation between variants of itself, and it outperforms a range of existing strategies including memory-$1$, memory-$2$, and those from a documented strategy library in evolutionary dynamics. Our work thus underscores the pivotal role of cumulative action consistency in enhancing cooperation, developing robust strategies, and offering cognitively low-burden information processing mechanisms in repeated social dilemmas.

q-bio.PE↗

arXiv2025

Differentiated Information Mining: A Semi-supervised Learning Framework for GNNs

In semi-supervised learning (SSL) for enhancing the performance of graph neural networks (GNNs) with unlabeled data, introducing mutually independent decision factors for cross-validation is regarded as an effective strategy to alleviate pseudo-label confirmation bias and training collapse. However, obtaining such factors is challenging in practice: additional and valid information sources are inherently scarce, and even when such sources are available, their independence from the original source cannot be guaranteed. To address this challenge, In this paper we propose a Differentiated Factor Consistency Semi-supervised Framework (DiFac), which derives differentiated factors from a single information source and enforces their consistency. During pre-training, the model learns to extract these factors; in training, it iteratively removes samples with conflicting factors and ranks pseudo-labels based on the shortest stave principle, selecting the top candidate samples to reduce overconfidence commonly observed in confidence-based or ensemble-based methods. Our framework can also incorporate additional information sources. In this work, we leverage the large multimodal language model to introduce latent textual knowledge as auxiliary decision factors, and we design a accountability scoring mechanism to mitigate additional erroneous judgments introduced by these auxiliary factors. Experiments on multiple benchmark datasets demonstrate that DiFac consistently improves robustness and generalization in low-label regimes, outperforming other baseline methods.

cs.LG↗

arXiv2025

CFDagent: A Language-Guided, Zero-Shot Multi-Agent System for Complex Flow Simulation

We introduce CFDagent, a zero-shot, multi-agent system that enables fully autonomous computational fluid dynamics (CFD) simulations from natural language prompts. CFDagent integrates three specialized LLM-driven agents: (i) the Preprocessing Agent that generates 3D geometries from textual or visual inputs using a hybrid text-to-3D diffusion model (Point-E) and automatically meshes the geometries; (ii) the Solver Agent that configures and executes an immersed boundary flow solver; and (iii) the Postprocessing Agent that analyzes and visualizes the results, including multimodal renderings. These agents are interactively guided by GPT-4o via conversational prompts, enabling intuitive and user-friendly interaction. We validate CFDagent by reproducing canonical sphere flows at Reynolds numbers of 100 and 300 using three distinct inputs: a simple text prompt (i.e., "sphere"), an image-based input, and a standard sphere model. The computed drag and lift coefficients from meshes produced by each input approach closely match available data. The proposed system enables synthesization of flow simulations and photorealistic visualizations for complex geometries. Through extensive tests on canonical and realistic scenarios, we demonstrate the robustness, versatility, and practical applicability of CFDagent. By bridging generative AI with high-fidelity simulations, CFDagent significantly lowers barriers to expert-level CFD, unlocking broad opportunities in education, scientific research, and practical engineering applications.

physics.flu-dyn↗

arXiv2025

Interactive Hybrid Rice Breeding with Parametric Dual Projection

Hybrid rice breeding crossbreeds different rice lines and cultivates the resulting hybrids in fields to select those with desirable agronomic traits, such as higher yields. Recently, genomic selection has emerged as an efficient way for hybrid rice breeding. It predicts the traits of hybrids based on their genes, which helps exclude many undesired hybrids, largely reducing the workload of field cultivation. However, due to the limited accuracy of genomic prediction models, breeders still need to combine their experience with the models to identify regulatory genes that control traits and select hybrids, which remains a time-consuming process. To ease this process, in this paper, we proposed a visual analysis method to facilitate interactive hybrid rice breeding. Regulatory gene identification and hybrid selection naturally ensemble a dual-analysis task. Therefore, we developed a parametric dual projection method with theoretical guarantees to facilitate interactive dual analysis. Based on this dual projection method, we further developed a gene visualization and a hybrid visualization to verify the identified regulatory genes and hybrids. The effectiveness of our method is demonstrated through the quantitative evaluation of the parametric dual projection method, identified regulatory genes and desired hybrids in the case study, and positive feedback from breeders.

cs.HC↗

arXiv2025

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment

We propose a novel method for aerial visual localization over low Level-of-Detail (LoD) city models. Previous wireframe-alignment-based method LoD-Loc has shown promising localization results leveraging LoD models. However, LoD-Loc mainly relies on high-LoD (LoD3 or LoD2) city models, but the majority of available models and those many countries plan to construct nationwide are low-LoD (LoD1). Consequently, enabling localization on low-LoD city models could unlock drones' potential for global urban localization. To address these issues, we introduce LoD-Loc v2, which employs a coarse-to-fine strategy using explicit silhouette alignment to achieve accurate localization over low-LoD city models in the air. Specifically, given a query image, LoD-Loc v2 first applies a building segmentation network to shape building silhouettes. Then, in the coarse pose selection stage, we construct a pose cost volume by uniformly sampling pose hypotheses around a prior pose to represent the pose probability distribution. Each cost of the volume measures the degree of alignment between the projected and predicted silhouettes. We select the pose with maximum value as the coarse pose. In the fine pose estimation stage, a particle filtering method incorporating a multi-beam tracking approach is used to efficiently explore the hypothesis space and obtain the final pose estimation. To further facilitate research in this field, we release two datasets with LoD1 city models covering 10.7 km , along with real RGB queries and ground-truth pose annotations. Experimental results show that LoD-Loc v2 improves estimation accuracy with high-LoD models and enables localization with low-LoD models for the first time. Moreover, it outperforms state-of-the-art baselines by large margins, even surpassing texture-model-based methods, and broadens the convergence basin to accommodate larger prior errors.

cs.CV↗