arXiv subjects

Zirui Chen

Publications and source records attributed to Zirui Chen.

At least 37 records · Page 2Linked to original sources

arXiv2026

Clumps in a Cocoon: Geometry and Mixing Set the Universal X-ray to H$α$ Surface Brightness Ratio

Recent observations reveal a universal X-ray to H$α$ surface-brightness ratio, ${\rm SB}_{\rm X}/{\rm SB}_{\rm Hα}\sim 3$, in galactic winds, ram-pressure stripped tails, and cluster filaments. This is surprising because H$α$ traces cold ($\sim 10^4$ K) gas while X-rays trace much hotter ($\sim 10^{6}$--$10^{7}$ K) gas. Plane-parallel mixing-layer models do not recover this ratio, and can be off by orders of magnitude. Motivated by recent work showing that geometry controls the temperature PDF of multiphase gas (Chen & Oh 2026), we run 3D wind-tunnel simulations in the high density contrast ($χ\sim 10^3$) regime. In this limit, the cold phase shatters into many small H$α$-emitting clumps, while X-ray-emitting gas forms a volume-filling cocoon around them. After smoothing on the tail-width scale, the measured surface-brightness ratio converges to the observed value, which can be understood theoretically. The H$α$ luminosity fraction is set by atomic physics, whereas the X-ray luminosity fraction is set by the residence time of gas in the X-ray-emitting band. This residence time is much shorter than the cooling time at X-ray temperatures, but scales roughly inversely with pressure, suggesting that it is tied to the cooling time at a lower-temperature outlet of the mixing cascade. This framework naturally explains why the observed ratio is order unity, and robust to changes in gas pressure.

astro-ph.GA↗

arXiv2026

Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection

Given its ability to reduce annotation costs, weakly supervised learning based on single-point annotations has emerged as a research focus in oriented object detection. Compared with the classical teacher-student paradigm, the simple model paradigm (e.g., PointOBB-v2) can substantially further reduce resources required for training while ensuring strong performance. The latter exhibits greater potential for low-cost training, yet such methods still face challenges of insufficient sample assignment and poor pseudo-label quality. In this paper, we propose a training-efficient framework named SSP, which synergizes rule-driven prior injection and data-driven label purification. Specifically, SSP introduces two designs: (1) Pixel-level Spatial Partition-based Sample Assignment, which compactly estimates the upper and lower bounds of object scales and mines high-quality positive samples and hard negative samples through spatial partitioning of pixel maps. (2) Semantic Spatial Partition-based Box Extraction, which derives instances from spatial partitions modulated by semantic maps and converts them into pseudo-boxes for supervising detectors. Experiments on DOTA-v1.0 and other datasets demonstrate SSP's superiority: it achieves +6.73% mAP improvement compared with the baseline, while requiring only 2 h of training time and 6 GB of GPU memory. Furthermore, when SSP is integrated with stronger detector, the mAP can reach 50.81%. The code is available at https://github.com/antxinyuan/ssp.

cs.CV↗

arXiv2026

ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models

Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond traditional embedding-based approaches. However, existing LLM-based methods often struggle to fully exploit structured semantic representations, as the continuous embedding space of pretrained KG models is fundamentally misaligned with the discrete token space of LLMs. This discrepancy hinders effective semantic transfer and limits their performance. To address this challenge, we propose ReaLM, a novel and effective framework that bridges the gap between KG embeddings and LLM tokenization through the mechanism of residual vector quantization. ReaLM discretizes pretrained KG embeddings into compact code sequences and integrates them as learnable tokens within the LLM vocabulary, enabling seamless fusion of symbolic and contextual knowledge. Furthermore, we incorporate ontology-guided class constraints to enforce semantic consistency, refining entity predictions based on class-level compatibility. Extensive experiments on two widely used benchmark datasets demonstrate that ReaLM achieves state-of-the-art performance, confirming its effectiveness in aligning structured knowledge with large-scale language models.

cs.CL↗

arXiv2026

Implicit Action Chunking for Smooth Continuous Control

Reinforcement learning often produces high-frequency oscillatory control signals that undermine the safety and stability required for physical deployment. Explicit action chunking addresses this by predicting fixed-horizon trajectories but scales the policy output dimension proportionally with the horizon length, leading to optimization difficulties and incompatibility with standard step-wise interaction. To overcome these challenges, this paper proposes Dual-Window Smoothing (DWS), an implicit action chunking framework for smooth continuous control. Unlike explicit methods, DWS enforces temporal coherence without expanding the action space. It uses a dual-window design: an execution window that ensures physical smoothness through deterministic modulation, and a value window that aligns temporal-difference targets over the horizon to correct critic bias caused by open-loop execution. DWS also includes a lightweight actor-side temporal regularizer based on first-order action differences to promote global continuity. This design effectively bridges the gap between temporal abstraction and reactive step-wise control. Experiments on benchmarks including the DeepMind Control Suite and industrial energy management tasks show that DWS outperforms state-of-the-art (SOTA) baselines. In complex vision-based autonomous driving tasks, DWS achieves smoother control, safer behavior with reduced jitter, and attains a 100% success rate.

cs.RO↗

arXiv2026

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt

Channel state information (CSI) is critical for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Pilot-based channel estimation methods suffer from high pilot overhead and low channel acquisition quality, while pilot-free approaches typically impose impractical demands on positional or environmental information precision. This paper proposes geometry-aided channel deduction (GCD), which leverages readily available geometric information to assist channel acquisition. The environmental map and base station position together constitute the scenario geometry, which can provide geometric channel features through ray tracing. To obtain the complete channel, the user first retrieves approximate geometric features by performing neighborhood searching within a pre-extracted geometric feature set, and then converts them into pseudo channels through a priori designed feature alignment. These pseudo channels serve as contextual prompt, providing supplementary channel features beyond those derived from pilot-based estimate. Finally, a neural network fuses these pseudo channels with partial estimate to generate the complete channel. Comprehensive experiments validate the superiority of our method, which achieves the leading accuracy in channel acquisition under sparse pilot conditions, demonstrates strong generalization capabilities in new scenarios and dynamic environments, and exhibits robust resilience against user position errors and non-ideal environmental information.

eess.SP↗

arXiv2026

Guiding Vector Field Generation via Score-based Diffusion Model

Guiding Vector Fields (GVFs) are a powerful tool for robotic path following. However, classical methods assume smooth, ordered curves and fail when paths are unordered, multi-branch, or generated by probabilistic models. We propose a unified framework, termed the Score-Induced Guiding Vector Field (SGVF), which leverages score-based generative modeling to construct vector fields directly from data distributions. SGVF learns tangent fields from point clouds with unit-norm, orthogonality, and directional-consistency losses, ensuring geometric fidelity and control feasibility. This approach removes the reliance on ad-hoc path segmentation and enables guidance along complex topologies such as branching and pseudo-manifolds. The study establishes a correspondence between score vanishing in diffusion models and GVF singularities and highlights representational capacity near sharp path curvatures. Experiments on robotic navigation in planar environments demonstrate that SGVF achieves reliable path following in scenarios where classical GVFs fail, underscoring its potential as a bridge between generative modeling and geometric control. Code and experiment video are available at https://github.com/czr-gif/Guiding-Vector-Field-Generation-via-Score-based-Diffusion-Model.

cs.RO↗

arXiv2026

Modeling Emission-Line Surface Brightness in a Multiphase Galactic Wind: An O VI Case Study

We present a fast and robust analytic framework for predicting surface brightness (SB) of emission lines in galactic winds as a function of radius up to $\sim 100$ kpc out in the circum-galactic medium. We model multiphase structure in galactic winds by capturing emission from both the volume-filling hot phase (T $\sim 10^{6-7}$ K) and turbulent radiative mixing layers that host intermediate temperature gas at the boundaries of cold clouds (T $\sim 10^4$ K). Our multiphase framework makes significantly different predictions of emission signatures compared to traditional single-phase models and explains the paucity of OVI SB measurements in the literature. After accounting for ram pressure equilibrium between the cold clouds and hot wind in supersonic outflows, non-equilibrium ionization effects, and energy budgets other than mechanical energy from core-collapse supernovae, our OVI SB predictions qualitatively match observational results. Our framework provides constraints on the optimal galactic wind properties that facilitate OVI emission observations, including star formation rate surface density, hot phase mass loading factor, and thermalization efficiency factor. These constraints are consistent with existing observations and can help inform future target selections.

astro-ph.GA↗

arXiv2026

Multi-View Wireless Sensing via Conditional Generative Learning: Framework and Model Design

In this paper, we incorporate physical knowledge into learning-based high-precision target sensing using the multi-view channel state information (CSI) between multiple base stations (BSs) and user equipment (UEs). Such kind of multi-view sensing problem can be naturally cast into a conditional generation framework. To this end, we design a bipartite neural network architecture, the first part of which uses an elaborately designed encoder to fuse the latent target features embedded in the multi-view CSI, and then the second uses them as conditioning inputs of a powerful generative model to guide the target's reconstruction. Specifically, the encoder is designed to capture the physical correlation between the CSI and the target, and also be adaptive to the numbers and positions of BS-UE pairs. Therein the view-specific nature of CSI is assimilated by introducing a spatial positional embedding scheme, which exploits the structure of electromagnetic(EM)-wave propagation channels. Finally, a conditional diffusion model with a weighted loss is employed to generate the target's point cloud from the fused features. Extensive numerical results demonstrate that the proposed generative multi-view (Gen-MV) sensing framework exhibits excellent flexibility and significant performance improvement on the reconstruction quality of target's shape and EM properties.

eess.SP↗

arXiv2026

Every Maintenance Has Its Exemplar: The Future of Software Maintenance through Migration

Maintenance is a critical stage in the software lifecycle, ensuring that post-release systems remain reliable, efficient, and adaptable. However, manual software maintenance is labor-intensive, time-consuming, and error-prone, which highlights the urgent need for automation. Learning from maintenance activities conducted on other software systems offers an effective way to improve efficiency. In particular, recent research has demonstrated that migration-based approaches transfer knowledge, artifacts, or solutions from one system to another and show strong potential in tasks such as API evolution adaptation, software testing, and migrating patches for fault correction. This makes migration-based maintenance a valuable research direction for advancing automated maintenance. This paper takes a step further by presenting the first systematic research agenda on migration-based approaches to software maintenance. We characterize the migration-based maintenance lifecycle through four key stages: \ding{182} identifying a maintenance task that can be addressed through migration, \ding{183} selecting suitable migration sources for the target project,\ding{184} matching relevant data across systems and adapting the migrated data to the target context, and \ding{185} validating the correctness of the migration. We also analyze the challenges that may arise at each stage. Our goal is to encourage the community to explore migration-based approaches more thoroughly and to tackle the key challenges that must be solved to advance automated software maintenance.

cs.SE↗

arXiv2025

Descent-Net: Learning Descent Directions for Constrained Optimization

Deep learning approaches, known for their ability to model complex relationships and fast execution, are increasingly being applied to solve large optimization problems. However, existing methods often face challenges in simultaneously ensuring feasibility and achieving an optimal objective value. To address this issue, we propose Descent-Net, a neural network designed to learn an effective descent direction from a feasible solution. By updating the solution along this learned direction, Descent-Net improves the objective value while preserving feasibility. Our method demonstrates strong performance on both synthetic optimization tasks and the real-world AC optimal power flow problem, while also exhibiting effective scalability to large problems, as shown by portfolio optimization experiments with thousands of assets.

math.OC↗

arXiv2025

Communication-Computation Pipeline Parallel Split Learning over Wireless Edge Networks

Split learning (SL) offloads main computing tasks from multiple resource-constrained user equippments (UEs) to the base station (BS), while preserving local data privacy. However, its computation and communication processes remain sequential, resulting in limited system efficiency. To overcome this limitation, this paper applies pipeline parallelism (PP) of distributed training to SL in wireless networks, proposing the so-called communication-computation pipeline parallel split learning (C$^2$P$^2$SL). By considering the communicating and computing processes of UEs and BS as an overall pipeline, C$^2$P$^2$SL achieves pipeline parallelization among different micro-batches which are split from each batch of data samples. The overlap of communication and computation in this way significantly reduces the total training time. Given that training efficiency is affected by position of cutting layer and heterogeneity of the UEs, we formulate a joint optimization problem of task split and resource allocation, and design a solution based on alternating optimization. Experimental results demonstrate that C$^2$P$^2$SL significantly reduces system training time by over 38\% while maintaining convergence accuracy under different communication conditions.

cs.DC↗

arXiv2025

Ontology-Enhanced Knowledge Graph Completion using Large Language Models

Large Language Models (LLMs) have been extensively adopted in Knowledge Graph Completion (KGC), showcasing significant research advancements. However, as black-box models driven by deep neural architectures, current LLM-based KGC methods rely on implicit knowledge representation with parallel propagation of erroneous knowledge, thereby hindering their ability to produce conclusive and decisive reasoning outcomes. We aim to integrate neural-perceptual structural information with ontological knowledge, leveraging the powerful capabilities of LLMs to achieve a deeper understanding of the intrinsic logic of the knowledge. We propose an ontology enhanced KGC method using LLMs -- OL-KGC. It first leverages neural perceptual mechanisms to effectively embed structural information into the textual space, and then uses an automated extraction algorithm to retrieve ontological knowledge from the knowledge graphs (KGs) that needs to be completed, which is further transformed into a textual format comprehensible to LLMs for providing logic guidance. We conducted extensive experiments on three widely-used benchmarks -- FB15K-237, UMLS and WN18RR. The experimental results demonstrate that OL-KGC significantly outperforms existing mainstream KGC methods across multiple evaluation metrics, achieving state-of-the-art performance.

cs.CL↗

arXiv2025

Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model

Research on leveraging big artificial intelligence model (BAIM) technology to drive the intelligent evolution of wireless networks is emerging. However, breakthroughs in generalization brought about by BAIM techniques mainly occur in natural language processing. There is a lack of a clear technical direction on how to efficiently apply BAIM techniques to wireless systems, which typically have many additional peculiarities. To this end, this paper reviews recent research on BAIM for wireless systems and assesses the current state of the field. It then analyzes and compares the differences between language intelligence and wireless intelligence on multiple levels, including scientific foundations, core usages, and technical details. It highlights the necessity and scientific significance of developing wireless native BAIM technologies, as well as specific issues that need to be considered for technical implementation. Finally, by synthesizing the evolutionary laws of language models with the particularities of wireless systems, this paper provides several instructive methodologies for developing wireless native BAIM.

eess.SP↗

arXiv2025

ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion

Knowledge graphs often suffer from incompleteness issues, which can be alleviated through information completion. However, current state-of-the-art deep knowledge convolutional embedding models rely on external convolution kernels and conventional convolution processes, which limits the feature interaction capability of the model. This paper introduces a novel dynamic convolutional embedding model, ConvD, which directly reshapes relation embeddings into multiple internal convolution kernels. This approach effectively enhances the feature interactions between relation embeddings and entity embeddings. Simultaneously, we incorporate a priori knowledge-optimized attention mechanism that assigns different contribution weight coefficients to the multiple relation convolution kernels in dynamic convolution, further boosting the expressive power of the model. Extensive experiments on various datasets show that our proposed model consistently outperforms the state-of-the-art baseline methods, with average improvements ranging from 3.28% to 14.69% across all model evaluation metrics, while the number of parameters is reduced by 50.66% to 85.40% compared to other state-of-the-art models.

cs.CL↗

arXiv2025

KG-BiLM: Knowledge Graph Embedding via Bidirectional Language Models

Recent advances in knowledge representation learning (KRL) highlight the urgent necessity to unify symbolic knowledge graphs (KGs) with language models (LMs) for richer semantic understanding. However, existing approaches typically prioritize either graph structure or textual semantics, leaving a gap: a unified framework that simultaneously captures global KG connectivity, nuanced linguistic context, and discriminative reasoning semantics. To bridge this gap, we introduce KG-BiLM, a bidirectional LM framework that fuses structural cues from KGs with the semantic expressiveness of generative transformers. KG-BiLM incorporates three key components: (i) Bidirectional Knowledge Attention, which removes the causal mask to enable full interaction among all tokens and entities; (ii) Knowledge-Masked Prediction, which encourages the model to leverage both local semantic contexts and global graph connectivity; and (iii) Contrastive Graph Semantic Aggregation, which preserves KG structure via contrastive alignment of sampled sub-graph representations. Extensive experiments on standard benchmarks demonstrate that KG-BiLM outperforms strong baselines in link prediction, especially on large-scale graphs with complex multi-hop relations - validating its effectiveness in unifying structural information and textual semantics.

cs.CL↗

arXiv2025

Large Language Model Enhanced Knowledge Representation Learning: A Survey

Knowledge Representation Learning (KRL) is crucial for enabling applications of symbolic knowledge from Knowledge Graphs (KGs) to downstream tasks by projecting knowledge facts into vector spaces. Despite their effectiveness in modeling KG structural information, KRL methods are suffering from the sparseness of KGs. The rise of Large Language Models (LLMs) built on the Transformer architecture presents promising opportunities for enhancing KRL by incorporating textual information to address information sparsity in KGs. LLM-enhanced KRL methods, including three key approaches, encoder-based methods that leverage detailed contextual information, encoder-decoder-based methods that utilize a unified Seq2Seq model for comprehensive encoding and decoding, and decoder-based methods that utilize extensive knowledge from large corpora, have significantly advanced the effectiveness and generalization of KRL in addressing a wide range of downstream tasks. This work provides a broad overview of downstream tasks while simultaneously identifying emerging research directions in these evolving domains.

cs.CL↗

arXiv2025

Generating Mitigations for Downstream Projects to Neutralize Upstream Library Vulnerability

Third-party libraries are essential in software development as they prevent the need for developers to recreate existing functionalities. However, vulnerabilities within these libraries pose significant risks to dependent projects. Upgrading dependencies to secure versions is not feasible to neutralize vulnerabilities without patches or in projects with specific version requirements. Moreover, repairing the vulnerability proves challenging when the source code of the library is inaccessible. Both the state-of-the-art automatic vulnerability repair and automatic program repair methods fail to address this issue. Therefore, mitigating library vulnerabilities without source code and available patches is crucial for a swift response to potential security attacks. Existing tools encounter challenges concerning generalizability and functional security. In this study, we introduce LUMEN to mitigate library vulnerabilities in impacted projects. Upon disclosing a vulnerability, we retrieve existing workarounds to gather a resembling mitigation strategy. In cases where a resembling strategy is absent, we propose type-based strategies based on the vulnerability reproducing behavior and extract essential information from the vulnerability report to guide mitigation generation. Our assessment of LUMEN spans 121 impacted functions of 40 vulnerabilities, successfully mitigating 70.2% of the functions, which substantially outperforms our baseline in neutralizing vulnerabilities without functionality loss. Additionally, we conduct an ablation study to validate the rationale behind our resembling strategies and type-based strategies.

cs.SE↗

arXiv2025

Vulnerability-Triggering Test Case Generation from Third-Party Libraries

Open-source third-party libraries are widely used in software development. These libraries offer substantial advantages in terms of time and resource savings. However, a significant concern arises due to the publicly disclosed vulnerabilities within these libraries. Existing automated vulnerability detection tools often suffer from false positives and fail to accurately assess the propagation of inputs capable of triggering vulnerabilities from client projects to vulnerable code in libraries. In this paper, we propose a novel approach called VULEUT (Vulnerability Exploit Unit Test Generation), which combines vulnerability exploitation reachability analysis and LLM-based unit test generation. VULEUT is designed to automatically verify the exploitability of vulnerabilities in third-party libraries commonly used in client software projects. VULEUT first analyzes the client projects to determine the reachability of vulnerability conditions. And then, it leverages the Large Language Model (LLM) to generate unit tests for vulnerability confirmation. To evaluate the effectiveness of VULEUT, we collect 32 vulnerabilities from various third-party libraries and conduct experiments on 70 real client projects. Besides, we also compare our approach with two representative tools, i.e., TRANSFER and VESTA. Our results demonstrate the effectiveness of VULEUT, with 229 out of 292 generated unit tests successfully confirming vulnerability exploitation across 70 client projects, which outperforms baselines by 24%.

cs.SE↗