arXiv subjects

Ming Dong

Publications and source records attributed to Ming Dong.

At least 19 recordsLinked to original sources

arXiv2026

Excitation of non-modal perturbations in hypersonic boundary layers by free stream forcing. Part II: asymptotic theory and key mechanisms

Recently, Zhao & Dong (J. Fluid Mech. 2025, vol. 1013: A44) developed a high-efficiency, high-accuracy numerical framework, the shock-fitting harmonic linearised Navier-Stokes (SF-HLNS) approach, which enables a systematic study of the receptivity of non-modal perturbations in hypersonic blunt-body boundary layers over a wide parameter range. In this Part II, we employ a high-Reynolds-number asymptotic analysis to elucidate the physical mechanism of the receptivity process. A distinct slow-down convection mechanism is identified in the nose region, amplifying the perturbation streamwise vorticity from the post-shock position to the boundary layer around the stagnation point by a factor of O(\sqrt{R}), where R is the Reynolds number based on nose radius. Downstream, the lift-up mechanism further leads to a transient growth of the perturbation streamwise velocity up to an amplitude of O(R). Based on these mechanisms, a reduced model is developed to predict the downstream evolution of the non-modal perturbations initiated by receptivity, whose predictions agree well with SF-HLNS calculations. This model can also be used to investigate the effects of wall temperature and nose radius on non-modal receptivity efficiency, as will be detailed in Part III of this work series.

physics.flu-dyn↗

arXiv2026

CoeusBI: A Comprehensive Interactive Business Intelligence System Powered by LLMs at Baidu [Extended Version]

The advent of Large Language Models has catalyzed the emergence of interactive Business Intelligence (BI) systems. Although commercial BI products increasingly adopt semantic layers paired with natural language interfaces, they predominantly rely on manual configurations to define metrics and dimensions. Real-world deployments face critical challenges: (a) frequent JOIN operations degrade the accuracy of SQL generation; (b) wide schemas exacerbate the challenge of schema linking; and (c) the generation of dialect-specific queries and the accurate support for multi-round dialogues incur high computational costs and yield limited accuracy. We introduce CoeusBI, an industrial-scale interactive BI system that addresses these barriers through a novel Dual-Agent Architecture paired with a Hierarchical Schema Linking module: (1) an offline View Generation Agent that utilizes error-feedback to autonomously convert complex JOIN queries into simple single-view queries, which eliminates the need for manual semantic modeling; (2) a Hierarchical Schema Linking module that leverages vector retrieval over views to handle exceptionally wide schemas efficiently; and (3) a dynamic Routing Agent that evaluates dialogue contexts to route queries, dynamically invoking either the synthesis of new intermediate representations or targeted modifications of existing ones, before compiling the unified representation via a deterministic SQL compiler that is agnostic to dialects. Extensive experiments on both public datasets and production datasets demonstrate that CoeusBI achieves significant improvements in query accuracy, token efficiency, and user satisfaction relative to existing methods. CoeusBI is deployed as a standalone service on the data platform of Baidu and is widely used across multiple business lines supporting thousands of users daily, thereby evidencing strong practicality and scalability.

cs.DB↗

arXiv2026

EvoSQL: Memory-Augmented Critic-Generator Co-Evolution for Text-to-SQL

Text-to-SQL has advanced rapidly with large language models, but complex database queries still require reasoning beyond one-shot generation, including multi-step decomposition, execution-based diagnosis, and targeted correction. We present EvoSQL, a co-evolution framework that formulates SQL synthesis as an iterative interaction between a generator and a critic. EvoSQL maintains a contextualized candidate memory, verifies SQL candidates with both execution signals and LLM-based critique, and updates its memory through utility-guided aggregation. To strengthen the underlying generator-critic pair, we further introduce a Self-Distillation Policy Optimization (SDPO) fine-tuning stage that injects execution-aware supervision into modern coding LLM backbones. Experiments on Spider and BIRD show that EvoSQL consistently improves open-source models over Maj@16 baselines, with particularly large gains on BIRD-Dev, ranging from +1.37% for Qwen3-4B to +9.19% for Qwen2.5-Coder-3B. SDPO initialization further improves selected backbones on Spider-Test and BIRD-Dev. These results suggest that memory-grounded co-evolution is an effective path toward more reliable and generalizable Text-to-SQL systems. Code is available at https://github.com/valleysprings/EvoSQL.

cs.AI↗

arXiv2026

On the Robustness of Knowledge Editing for Detoxification

Knowledge-Editing-based (KE-based) detoxification has emerged as a promising approach for mitigating harmful behaviours in Large Language Models. Existing evaluations, however, largely rely on automatic toxicity classifiers, implicitly assuming that reduced toxicity scores reflect genuine behavioural suppression. In this work, we propose a robustness-oriented evaluation framework for KE-based detoxification that examines its reliability beyond standard classifier-based metrics along three dimensions: optimisation robustness, compositional robustness, and cross-lingual robustness. We identify pseudo-detoxification as a common failure mode, where apparent toxicity reductions arise from degenerate generation behaviours rather than meaningful suppression of unsafe content. We further show that detoxification effectiveness degrades when multiple unsafe behaviours are edited jointly, and that both monolingual and cross-lingual detoxification remain effective only under specific model-method combinations. Overall, our results indicate that KE-based detoxification is robust only for certain models, limited numbers of detoxification objectives, and a subset of languages.

cs.CL↗

arXiv2026

From Horizontal Layering to Vertical Integration: A Comparative Study of the AI-Driven Software Development Paradigm

This paper examines the organizational implications of Generative AI adoption in software engineering through a multiple-case comparative study. We contrast two development environments: a traditional enterprise (brownfield) and an AI-native startup (greenfield). Our analysis reveals that transitioning from Horizontal Layering (functional specialization) to Vertical Integration (end-to-end ownership) yields 8-fold to 33-fold reductions in resource consumption. We attribute these gains to the emergence of Super Employees, AI-augmented engineers who span traditional role boundaries, and the elimination of inter-functional coordination overhead. Theoretically, we propose Human-AI Collaboration Efficacy as the primary optimization target for engineering organizations, supplanting individual productivity metrics. Our Total Factor Productivity analysis identifies an AI Distortion Effect that diminishes returns to labor scale while amplifying technological leverage. We conclude with managerial strategies for organizational redesign, including the reactivation of idle cognitive bandwidth in senior engineers and the suppression of blind scale expansion.

cs.SE↗

arXiv2025

Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual Approximators

Image-to-Image (I2I) translation involves converting an image from one domain to another. Deterministic I2I translation, such as in image super-resolution, extends this concept by guaranteeing that each input generates a consistent and predictable output, closely matching the ground truth (GT) with high fidelity. In this paper, we propose a denoising Brownian bridge model with dual approximators (Dual-approx Bridge), a novel generative model that exploits the Brownian bridge dynamics and two neural network-based approximators (one for forward and one for reverse process) to produce faithful output with negligible variance and high image quality in I2I translations. Our extensive experiments on benchmark datasets including image generation and super-resolution demonstrate the consistent and superior performance of Dual-approx Bridge in terms of image quality and faithfulness to GT when compared to both stochastic and deterministic baselines. Project page and code: https://github.com/bohan95/dual-app-bridge

cs.CV↗

arXiv2025

Prediction of bypass transition in hypersonic blunt-plate boundary layers subject to noisy conditions

In hypersonic boundary-layer flows over blunt bodies, laminar-turbulent transition exhibits two distinct regimes: for small nose radii, increased bluntness delays transition; beyond a critical radius, further increasing bluntness reverses this trend. The latter regime corresponds to a bypass transition route, whose onset remains challenging to predict. The primary difficulty lies in capturing the excitation of non-modal streaks in the nose region, which is strongly affected by the bow shock and entropy layer effects. Recently, Zhao & Dong (J. Fluid Mech., 2025, 1013: A44) develops a high-efficient, high-accuracy shock-fitting harmonic linearised Navier-Stokes (SF-HLNS) approach to quantify the excitation of linear non-modal perturbations. In this paper, we present a predictive framework for bypass transition by integrating the SF-HLNS approach with the nonlinear parabolised stability equations (NPSE) and the bi-global stability analysis (BSA). The NPSE is employed to track the nonlinear evolution of the streaky perturbations up to nonlinear saturation, while the BSA approach is used to capture the high-growth secondary instabilities. By integrating the growth rates of these secondary instability from their neutral positions, an amplitude amplification factor is obtained, enabling the prediction of transition onset. Under slow-acoustic forcing conditions drawn from wind-tunnel experiments, the present hybrid framework successfully reproduces the transition-reversal phenomenon at large nose radii, and yields quantitative agreement with measured transition locations, thereby validating its predictive capability.

physics.flu-dyn↗

arXiv2025

Regularized Schr\"odinger Bridge via Distortion-Perception Perturbation for High-Fidelity Speech Enhancement

Speech enhancement (SE) requires high-fidelity reconstruction of clean speech that preserves linguistic and paralinguistic cues while maintaining high perceptual quality. Recently, Schr\"odinger Bridge (SB), a family of diffusion-based generative models, has advanced SE by bridging degraded and clean speech distributions in a principled formulation, enabling higher-quality reconstructions with fewer sampling steps. However, diffusion-based SE methods still face two challenges: (1) the fidelity-realism tradeoff, where they often prioritize perceptual realism encouraged by the learned speech prior, at the expense of fidelity; and (2) the exposure bias issue, where iterative multi-step sampling causes early-step prediction errors to accumulate along the sampling trajectory and degrade enhanced speech quality. In this paper, we analyze standard SB training and show that it induces a systematic prediction drift, which biases the multi-step trajectory and amplifies error accumulation. To address this, we propose Regularized Schr\"odinger Bridge (RSB) for high-fidelity SE, a generative approach that reconciles fidelity and realism while mitigating exposure bias. RSB regularizes training with a Distortion-Perception Perturbation that constructs time-varying targets by interpolating between clean speech and posterior-mean estimates, and trains the network on perturbed intermediate states to correct toward the ground truth progressively. By simulating inference-time prediction errors, this perturbation mitigates the training-inference mismatch and thereby alleviates exposure bias. It also injects posterior-mean estimates as fidelity-preserving guidance, thereby improving reconstruction fidelity.

cs.LG↗

arXiv2025

DSCD: Large Language Model Detoxification with Self-Constrained Decoding

Detoxification in large language models (LLMs) remains a significant research challenge. Existing decoding detoxification methods are all based on external constraints, which require additional resource overhead and lose generation fluency. This work proposes Detoxification with Self-Constrained Decoding (DSCD), a novel method for LLM detoxification without parameter fine-tuning. DSCD strengthens the inner next-token distribution of the safety layer while weakening that of hallucination and toxic layers during output generation. This effectively diminishes toxicity and enhances output safety. DSCD offers lightweight, high compatibility, and plug-and-play capabilities, readily integrating with existing detoxification methods for further performance improvement. Extensive experiments on representative open-source LLMs and public datasets validate DSCD's effectiveness, demonstrating state-of-the-art (SOTA) performance in both detoxification and generation fluency, with superior efficiency compared to existing methods. These results highlight DSCD's potential as a practical and scalable solution for safer LLM deployments.

cs.CL↗

arXiv2025

On the classical solution for the steady triple-deck equations

This paper establishes the existence and uniqueness of classical solutions to the steady Triple-Deck equations, which describe incompressible boundary layer flow over localized roughness at high Reynolds numbers. The triple-deck theory was developed to overcome the Goldstein singularity in classical Prandtl boundary layer theory, capturing the interaction between the viscous sublayer, main layer, and upper layer when surface roughness of height $O({\rm Re}^{-\frac58})$ is present. The key ingredients of this paper include: (1) A decomposition separating roughness effects from nonlinear difficulties; (2) A novel Green's function using Airy functions that overcomes low-frequency singularities via the non-vanishing property of $\sqrt{3}Ai(z)+Bi(z)$; (3) The introduction of weighted Sobolev norms $\norm{|\p_x|^{\frac{1}{18}}y^{\frac16}\omega}_{L^2}$ of the vorticity yielding $M$-independent estimates for displacement $A$. As a byproduct, local uniqueness of Couette flow is established when $F=0$.

math.AP↗

arXiv2025

Asymptotic-analysis-inspired boundary conditions aiming at eliminating polymer diffusive instability

The recent discovery of polymer diffusive instability (PDI) by Beneitez et al. (Phys. Rev. Fluids, 2023, 8: L101901) poses challenges in implementing artificial conformation diffusion (ACD) in transition simulations of viscoelastic wall-shear flows. In this paper, we demonstrate that the unstable PDI is primarily induced by the conformation boundary conditions additionally introduced in the ACD equation system, which could be eliminated if a new set of conformation conditions is adopted. To address this issue, we begin with an asymptotic analysis of the PDI within the near-wall thin diffusive layer, which simplifies the complexity of the instability system by reducing the number of the controlling parameters from five to zero. Then, based on this simplified model, we construct a stable asymptotic solution that minimises the perturbations in the wall sublayer. From the near-wall behaviour of this solution, we derive a new set of conformation boundary conditions, prescribing a Neumann-type condition for its streamwise stretching component, $c_{11}$, and Dirichlet-type conditions for all the other conformation components. These boundary conditions are subsequently validated within the original ACD instability system, incorporating both the Oldroyd-B and FENE-P constitutive models. Finally, we perform direct numerical simulations based on the traditional and the new conformation conditions, demonstrating the effectiveness of the latter in eliminating the unstable PDI. Importantly, this improvement does not affect the calculations of other types of instabilities. Therefore, this work offers a promising approach for achieving reliable polymer-flow simulations with ACD, ensuring both numerical stability and accuracy.

physics.flu-dyn↗

arXiv2025

Modality-AGnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentation

Cardiac substructure delineation is emerging in treatment planning to minimize the risk of radiation-induced heart disease. Deep learning offers efficient methods to reduce contouring burden but currently lacks generalizability across different modalities and overlapping structures. This work introduces and validates a Modality-AGnostic Image Cascade (MAGIC) deep-learning pipeline for comprehensive and multi-modal cardiac substructure segmentation. MAGIC is implemented through replicated encoding and decoding branches of an nnU-Net backbone to handle multi-modality inputs and overlapping labels. First benchmarked on the multi-modality whole-heart segmentation (MMWHS) dataset including cardiac CT-angiography (CCTA) and MR modalities, twenty cardiac substructures (heart, chambers, great vessels (GVs), valves, coronary arteries (CAs), and conduction nodes) from clinical simulation CT (Sim-CT), low-field MR-Linac, and cardiac CT-angiography (CCTA) modalities were delineated to train semi-supervised (n=151), validate (n=15), and test (n=30) MAGIC. For comparison, fourteen single-modality comparison models (two MMWHS modalities and four subgroups across three clinical modalities) were trained. Methods were evaluated for efficiency and against reference contours through the Dice similarity coefficient (DSC) and two-tailed Wilcoxon Signed-Rank test (p<0.05). Average MMWHS DSC scores across CCTA and MR inputs were 0.88(0.08) and 0.87(0.04) respectively with significant improvement over unimodal baselines. Average 20-structure DSC scores were 0.75(0.16) for Sim-CT, 0.68(0.21) for MR-Linac, and 0.80(0.16) for CCTA. Furthermore, >80% and >70% reductions in training time and parameters were achieved, respectively. MAGIC offers an efficient, lightweight solution capable of segmenting multiple image modalities and overlapping structures in a single model without compromising segmentation accuracy.

physics.med-ph↗

arXiv2025

FISH-Tuning: Enhancing PEFT Methods with Fisher Information

The rapid growth in the parameter size of Large Language Models (LLMs) has spurred the development of Parameter-Efficient Fine-Tuning (PEFT) methods to mitigate the substantial computational costs of fine-tuning. Among these, Fisher Induced Sparse uncHanging (FISH) Mask is a selection-based PEFT technique that identifies a critical subset of pre-trained parameters using approximate Fisher information. While addition-based and reparameterization-based PEFT methods like LoRA and Adapter already fine-tune only a small number of parameters, the newly introduced parameters within these methods themselves present an opportunity for further optimization. Selectively fine-tuning only the most impactful among these new parameters could further reduce resource consumption while maintaining, or even improving, fine-tuning effectiveness. In this paper, we propose \textbf{FISH-Tuning}, a novel approach that incorporates FISH Mask into such PEFT methods, including LoRA, Adapter, and their variants. By leveraging Fisher information to identify and update only the most significant parameters within these added or reparameterized components, FISH-Tuning aims to achieve superior performance without increasing training time or inference latency compared to the vanilla PEFT methods. Experimental results across various datasets and pre-trained models demonstrate that FISH-Tuning consistently outperforms the vanilla PEFT methods when using the same proportion of trainable parameters. Code is available at https://anonymous.4open.science/r/FISH-Tuning-6F7C.

cs.CL↗

arXiv2025

Deterministic Medical Image Translation via High-fidelity Brownian Bridges

Recent studies have shown that diffusion models produce superior synthetic images when compared to Generative Adversarial Networks (GANs). However, their outputs are often non-deterministic and lack high fidelity to the ground truth due to the inherent randomness. In this paper, we propose a novel High-fidelity Brownian bridge model (HiFi-BBrg) for deterministic medical image translations. Our model comprises two distinct yet mutually beneficial mappings: a generation mapping and a reconstruction mapping. The Brownian bridge training process is guided by the fidelity loss and adversarial training in the reconstruction mapping. This ensures that translated images can be accurately reversed to their original forms, thereby achieving consistent translations with high fidelity to the ground truth. Our extensive experiments on multiple datasets show HiFi-BBrg outperforms state-of-the-art methods in multi-modal image translation and multi-image super-resolution.

eess.IV↗

arXiv2025

Excitation of non-modal perturbations in hypersonic boundary layers by freestream forcing: shock-fitting harmonic linearised Navier-Stokes approach

In this paper, we study the receptivity of non-modal perturbations in hypersonic boundary layers over a blunt wedge subject to freestream vortical, entropy and acoustic perturbations. Due to the absence of the Mack-mode instability and the rather weak entropy-layer instability, the non-modal perturbation is considered as the dominant factor triggering transition. This is a highly intricate problem, given the complexities arising from the presence of the bow shock, entropy layer, and their interactions with oncoming disturbances. To tackle this challenge, we develop a novel numerical tool, the shock-fitting harmonic linearized Navier-Stokes (SF-HLNS) approach, which offers a comprehensive investigation on the dependence of the receptivity efficiency on the nose bluntness and properties of the freestream forcing. The numerical findings suggest that the non-modal perturbations are more susceptible to freestream acoustic and entropy perturbations compared to the vortical perturbations, with the optimal spanwise length scale being comparable with the downstream boundary-layer thickness. Notably, as the nose bluntness increases, the receptivity to the acoustic and entropy perturbations intensifies, reflecting the transition reversal phenomenon observed experimentally in configurations with relatively large bluntness. In contrast, the receptivity to freestream vortical perturbations weakens with increasing bluntness. Additionally, through the SF-HLNS calculations, we examine the credibility of the optimal growth theory (OGT) on describing the evolution of non-modal perturbations. Its accuracy in predicting the early-stage evolution and the energy amplification proves to be unreliable. Given its high-efficiency and high-accuracy nature, the SF-HLNS approach shows great potential as a valuable tool for conducting future research on hypersonic blunt-body boundary-layer transition.

physics.flu-dyn↗

arXiv2025

Conditional diffusion model with spatial attention and latent embedding for medical image segmentation

Diffusion models have been used extensively for high quality image and video generation tasks. In this paper, we propose a novel conditional diffusion model with spatial attention and latent embedding (cDAL) for medical image segmentation. In cDAL, a convolutional neural network (CNN) based discriminator is used at every time-step of the diffusion process to distinguish between the generated labels and the real ones. A spatial attention map is computed based on the features learned by the discriminator to help cDAL generate more accurate segmentation of discriminative regions in an input image. Additionally, we incorporated a random latent embedding into each layer of our model to significantly reduce the number of training and sampling time-steps, thereby making it much faster than other diffusion models for image segmentation. We applied cDAL on 3 publicly available medical image segmentation datasets (MoNuSeg, Chest X-ray and Hippocampus) and observed significant qualitative and quantitative improvements with higher Dice scores and mIoU over the state-of-the-art algorithms. The source code is publicly available at https://github.com/Hejrati/cDAL/.

eess.IV↗

arXiv2024

Instability in centrifugally stable shear flows

We investigate the linear instability of flows that are stable according to Rayleigh's criterion for rotating fluids. Using Taylor-Couette flow as a primary test case, we develop large Reynolds number matched asymptotic expansion theories. Our theoretical results not only aid in detecting instabilities previously reported by Deguchi (2017) across a wide parameter range but also clarify the physical mechanisms behind this counterintuitive phenomenon. Instability arises from the interaction between large-scale inviscid vortices and the viscous flow structure near the wall, which is analogous to Tollmien-Schlichting waves. Furthermore, our asymptotic theories and numerical computations reveal that similar instability mechanisms occur in boundary layer flows over convex walls.

physics.flu-dyn↗

arXiv2024

ChatBI: Towards Natural Language to Complex Business Intelligence SQL

The Natural Language to SQL (NL2SQL) technology provides non-expert users who are unfamiliar with databases the opportunity to use SQL for data analysis.Converting Natural Language to Business Intelligence (NL2BI) is a popular practical scenario for NL2SQL in actual production systems. Compared to NL2SQL, NL2BI introduces more challenges. In this paper, we propose ChatBI, a comprehensive and efficient technology for solving the NL2BI task. First, we analyze the interaction mode, an important module where NL2SQL and NL2BI differ in use, and design a smaller and cheaper model to match this interaction mode. In BI scenarios, tables contain a huge number of columns, making it impossible for existing NL2SQL methods that rely on Large Language Models (LLMs) for schema linking to proceed due to token limitations. The higher proportion of ambiguous columns in BI scenarios also makes schema linking difficult. ChatBI combines existing view technology in the database community to first decompose the schema linking problem into a Single View Selection problem and then uses a smaller and cheaper machine learning model to select the single view with a significantly reduced number of columns. The columns of this single view are then passed as the required columns for schema linking into the LLM. Finally, ChatBI proposes a phased process flow different from existing process flows, which allows ChatBI to generate SQL containing complex semantics and comparison relations more accurately. We have deployed ChatBI on Baidu's data platform and integrated it into multiple product lines for large-scale production task evaluation. The obtained results highlight its superiority in practicality, versatility, and efficiency. At the same time, compared with the current mainstream NL2SQL technology under our real BI scenario data tables and queries, it also achieved the best results.

cs.DB↗