arXiv subjects

Yiqing Li

Publications and source records attributed to Yiqing Li.

At least 19 recordsLinked to original sources

arXiv2026

From Evaluated Models to Evaluation Aids: A Multi-Evidence Study of LLM-Based Difficulty Calibration for Programming Examinations

Difficulty differences across parallel-class programming examinations affect the fairness of course assessment. This study repositions large language models from benchmark evaluation targets to auxiliary evidence sources for interpreting exam difficulty, combining AI evidence with aggregated student performance, item exposure, online-judge process data, and teacher interpretation. First, ten models solved an eight-problem final exam synchronously with 120 students: AI pass rate correlated positively with student pass rate (Spearman rho = 0.866, exact p = 0.0119), and a solving-based composite difficulty index correlated negatively with it (rho = -0.905, exact p = 0.0046). A single structured reviewer was then run via auditable API calls on a third-party OpenAI-compatible endpoint whose model label (gpt-5.6-sol) cannot authenticate an official OpenAI upstream model; call metadata and raw responses are archived. Across 79 problems from 11 parallel-class final exams, AI overall difficulty correlated with problem-level pass rate at rho = -0.871 and with non-attempt rate at rho = 0.800; in a 26-problem longitudinal Data Structures and Algorithms B sample, the correlations were -0.829 and 0.883. A 106-problem introductory-course (CS101) sample marks the boundary: the problem-level correlation weakened to rho = -0.552, and the exam-level correlation across 16 exams was near zero, with cohort composition dominating exam-level outcomes. Exposure-discount (0-0.40) and duplicate-problem perturbation tests did not change these directions. AI evidence can thus serve as an external reference for problem validation, parallel-class fairness discussion, and longitudinal quality tracking, while the model-identity boundary, single-reviewer design, and review-output instability set explicit limits: AI difficulty scales must not be used for individual student evaluation or automatic grade adjustment.

cs.CY↗

arXiv2026

SEDGE: Structural Extrapolated Data Generation

This paper aims to address the challenge of data generation beyond the training data and proposes a framework for Structural Extrapolated Data GEneration (SEDGE) based on suitable assumptions on the underlying data-generating process. We provide conditions under which data satisfying novel specifications can be generated reliably, together with the approximate identifiability of the distribution of such data under certain ``conservative" assumptions, as well as the inherent non-identifiability of this distribution without such assumptions. On the algorithmic side, we develop practical methods to achieve extrapolated data generation, based on a structure-informed optimization strategy or diffusion posterior sampling, respectively. We verify the extrapolation performance on synthetic data and also consider extrapolated image generation as a real-world scenario to illustrate the validity of the proposed framework.

cs.LG↗

arXiv2026

FastPhysGS: Accelerating Physics-based Dynamic 3DGS Simulation via Interior Completion and Adaptive Optimization

Extending 3D Gaussian Splatting (3DGS) to 4D physical simulation remains challenging. Based on the Material Point Method (MPM), existing methods either rely on manual parameter tuning or distill dynamics from video diffusion models, limiting the generalization and optimization efficiency. Recent attempts using LLMs/VLMs suffer from a text/image-to-3D perceptual gap, yielding unstable physics behavior. In addition, they often ignore the surface structure of 3DGS, leading to implausible motion. We propose FastPhysGS, a fast and robust framework for physics-based dynamic 3DGS simulation:(1) Instance-aware Particle Filling (IPF) with Monte Carlo Importance Sampling (MCIS) to efficiently populate interior particles while preserving geometric fidelity; (2) Bidirectional Graph Decoupling Optimization (BGDO), an adaptive strategy that rapidly optimizes material parameters predicted from a VLM. Experiments show FastPhysGS achieves high-fidelity physical simulation in 1 minute using only 7 GB runtime memory, outperforming prior works with broad potential applications.

cs.CV↗

arXiv2025

Turbulence enhancement of a fan array wind generator using geometric texturing and optimization-based control

Fan array wind generators (FAWG) are designed to generate a rich set of turbulent flows reminiscent of those found in natural environments. In this study, we experimentally investigate a square FAWG consisting of 10x10 individually controllable fans with 4 cm width and a maximum velocity of 17 m/s. The goal is to maximize the turbulence intensity in the test region. Two approaches for fan operation are investigated: first, geometric texturing of the duty cycle distribution, and second, maximization of the turbulence intensity at selected hot-wire sensors with particle-swarm optimization. We find that geometric texturing (specifically a checkerboard pattern) yields a robust, uniform turbulence field (Tu ~ 0.14) driven by jet interactions. Conversely, particle swarm optimization achieves higher local turbulence (Tu ~ 0.28) but significantly sacrifices spatial uniformity. This study underscores the trade-off between local maximization and global uniformity in active turbulence generation.

physics.flu-dyn↗

arXiv2025

Fast and robust parametric and functional learning with Hybrid Genetic Optimisation (HyGO)

The Hybrid Genetic Optimisation framework (HYGO) is introduced to meet the pressing need for efficient and unified optimisation frameworks that support both parametric and functional learning in complex engineering problems. Evolutionary algorithms are widely employed as derivative-free global optimisation methods but often suffer from slow convergence rates, especially during late-stage learning. HYGO integrates the global exploration capabilities of evolutionary algorithms with accelerated local search for robust solution refinement. The key enabler is a two-stage strategy that balances exploration and exploitation. For parametric problems, HYGO alternates between genetic algorithm and targeted improvement through a degeneracy-proof Dowhill Simplex Method (DSM). For function optimisation tasks, HYGO rotates between genetic programming and DSM. Validation is performed on (a) parametric optimisation benchmarks, where HYGO demonstrates faster and more robust convergence than standard genetic algorithms, and (b) function optimisation tasks, including control of a damped Landau oscillator. Practical relevance is showcased through aerodynamic drag reduction of an Ahmed body via Reynolds-Averaged Navier-Stokes simulations, achieving consistently interpretable results and reductions exceeding 20% by controlled jet injection in the back of the body for flow reattachment and separation bubble reduction. Overall, HYGO emerges as a versatile hybrid optimisation framework suitable for a broad spectrum of engineering and scientific problems involving parametric and functional learning.

cs.NE↗

arXiv2025

GUI-ARP: Enhancing Grounding with Adaptive Region Perception for GUI Agents

Existing GUI grounding methods often struggle with fine-grained localization in high-resolution screenshots. To address this, we propose GUI-ARP, a novel framework that enables adaptive multi-stage inference. Equipped with the proposed Adaptive Region Perception (ARP) and Adaptive Stage Controlling (ASC), GUI-ARP dynamically exploits visual attention for cropping task-relevant regions and adapts its inference strategy, performing a single-stage inference for simple cases and a multi-stage analysis for more complex scenarios. This is achieved through a two-phase training pipeline that integrates supervised fine-tuning with reinforcement fine-tuning based on Group Relative Policy Optimization (GRPO). Extensive experiments demonstrate that the proposed GUI-ARP achieves state-of-the-art performance on challenging GUI grounding benchmarks, with a 7B model reaching 60.8% accuracy on ScreenSpot-Pro and 30.9% on UI-Vision benchmark. Notably, GUI-ARP-7B demonstrates strong competitiveness against open-source 72B models (UI-TARS-72B at 38.1%) and proprietary models.

cs.CV↗

arXiv2025

TGPO: Tree-Guided Preference Optimization for Robust Web Agent Reinforcement Learning

With the rapid advancement of large language models and vision-language models, employing large models as Web Agents has become essential for automated web interaction. However, training Web Agents with reinforcement learning faces critical challenges including credit assignment misallocation, prohibitively high annotation costs, and reward sparsity. To address these issues, we propose Tree-Guided Preference Optimization (TGPO), an offline reinforcement learning framework that proposes a tree-structured trajectory representation merging semantically identical states across trajectories to eliminate label conflicts. Our framework incorporates a Process Reward Model that automatically generates fine-grained rewards through subgoal progress, redundancy detection, and action verification. Additionally, a dynamic weighting mechanism prioritizes high-impact decision points during training. Experiments on Online-Mind2Web and our self-constructed C-WebShop datasets demonstrate that TGPO significantly outperforms existing methods, achieving higher success rates with fewer redundant steps.

cs.LG↗

arXiv2025

Design of the wavy wall in a partially heated channel using CFD simulations and human-assisted Bayesian optimization

This study explores heated wavy wall shape design in channel flow using machine learning, aiming to minimize temperature variation ($\sigma_T$) while limiting pressure loss ($\Delta p$). A cost function $J$ defined as a product of $\sigma_T$ and $\Delta p$ balances these competing objectives. Optimization is performed via Bayesian optimization (BO) coupled with Reynolds-Averaged Navier-Stokes (RANS) computations in an active learning loop involving up to 1000 subsequent iterations. Two shaping strategies are considered: a sinusoidal-type function defined by four parameters (two waviness amplitudes, wave count, and tilt), and a higher-dimensional approach employing a Piecewise Cubic Hermite Interpolation Polynomial (PCHIP) with 19 control points. Results show the sinusoidal design reduces $\sigma_T$ over $60$-fold but increases $\Delta p$ fourfold, while the PCHIP shape offers only a $15$-fold $\sigma_T$ reduction but with a twofold $\Delta p$ increase. Flow characteristics such as turbulent kinetic energy, pressure, temperature, and Nusselt number are examined for both optimal and suboptimal shapes along the Pareto front. The insights gained motivated a human-aided refinement of the BO result, leading to a further $17.7$\% reduction in $J$. This was achieved by replacing small-amplitude waviness periods with flat segments, which additionally significantly facilitates manufacturability.

physics.flu-dyn↗

arXiv2025

Equivariant U-Shaped Neural Operators for the Cahn-Hilliard Phase-Field Model

Phase separation in binary mixtures, governed by the Cahn-Hilliard equation, plays a central role in interfacial dynamics across materials science and soft matter. While numerical solvers are accurate, they are often computationally expensive and lack flexibility across varying initial conditions and geometries. Neural operators provide a data-driven alternative by learning solution operators between function spaces, but current architectures often fail to capture multiscale behavior and neglect underlying physical symmetries. Here we show that an equivariant U-shaped neural operator (E-UNO) can learn the evolution of the phase-field variable from short histories of past dynamics, achieving accurate predictions across space and time. The model combines global spectral convolution with a multi-resolution U-shaped architecture and regulates translation equivariance to align with the underlying physics. E-UNO outperforms standard Fourier neural operator and U-shaped neural operator baselines, particularly on fine-scale and high-frequency structures. By encoding symmetry and scale hierarchy, the model generalizes better, requires less training data, and yields physically consistent dynamics. This establishes E-UNO as an efficient surrogate for complex phase-field systems.

cs.LG↗

arXiv2025

Origin and control of pseudo-rotating spiral jets

Active flow control (AFC) methods for jet-type flows have been extensively explored since the 1970s. Spectacular examples demonstrating the AFC power and the beauty of fluid mechanics include bifurcating and blooming jets. Recent advances in machine learning-based optimization have enabled efficient exploration of high-dimensional AFC, revealing control solutions beyond human intuition. The present paper focuses on one such discovery: the pseudo-rotating spiral jet. This phenomenon manifests as separate branches disconnected from the main jet stream, formed by vortical structures aligned along curved paths rotating around the initial jet axis. We investigate the origin of these jet-type patterns and formulate new rules for their control, showing that spiral jets belong to a family of multi-armed jets observable only at specific control settings. Furthermore, we demonstrate how human perception of three-dimensional imagery depends on the observable domain and vortex lifetime. Notably, the apparent rotation of spiral arms - despite having a well-defined frequency - is an illusion arising from the tendency to connect neighboring moving objects into continuous patterns. In contrast to the chaotic behavior of small-scale turbulence, we show that large-scale flow motion resulting from AFC operating in a deterministic manner is only seemingly unpredictable. Through theoretical analysis and 3D simulations, we develop a remarkably simple yet precise kinematic model that captures the formation and motion of these vortical paths. This model replicates the outcomes of complex flow simulations, reproduces the apparent jet shape, and facilitates the identification of the actual pattern. The findings offer new perspectives for both academic researchers and industrial engineers.

physics.flu-dyn↗

arXiv2025

SparseGS-W: Sparse-View 3D Gaussian Splatting in the Wild with Generative Priors

Synthesizing novel views of large-scale scenes from unconstrained in-the-wild images is an important but challenging task in computer vision. Existing methods, which optimize per-image appearance and transient occlusion through implicit neural networks from dense training views (approximately 1000 images), struggle to perform effectively under sparse input conditions, resulting in noticeable artifacts. To this end, we propose SparseGS-W, a novel framework based on 3D Gaussian Splatting that enables the reconstruction of complex outdoor scenes and handles occlusions and appearance changes with as few as five training images. We leverage geometric priors and constrained diffusion priors to compensate for the lack of multi-view information from extremely sparse input. Specifically, we propose a plug-and-play Constrained Novel-View Enhancement module to iteratively improve the quality of rendered novel views during the Gaussian optimization process. Furthermore, we propose an Occlusion Handling module, which flexibly removes occlusions utilizing the inherent high-quality inpainting capability of constrained diffusion priors. Both modules are capable of extracting appearance features from any user-provided reference image, enabling flexible modeling of illumination-consistent scenes. Extensive experiments on the PhotoTourism and Tanks and Temples datasets demonstrate that SparseGS-W achieves state-of-the-art performance not only in full-reference metrics, but also in commonly used non-reference metrics such as FID, ClipIQA, and MUSIQ.

cs.CV↗

arXiv2025

MotionDiff: Training-free Zero-shot Interactive Motion Editing via Flow-assisted Multi-view Diffusion

Generative models have made remarkable advancements and are capable of producing high-quality content. However, performing controllable editing with generative models remains challenging, due to their inherent uncertainty in outputs. This challenge is praticularly pronounced in motion editing, which involves the processing of spatial information. While some physics-based generative methods have attempted to implement motion editing, they typically operate on single-view images with simple motions, such as translation and dragging. These methods struggle to handle complex rotation and stretching motions and ensure multi-view consistency, often necessitating resource-intensive retraining. To address these challenges, we propose MotionDiff, a training-free zero-shot diffusion method that leverages optical flow for complex multi-view motion editing. Specifically, given a static scene, users can interactively select objects of interest to add motion priors. The proposed Point Kinematic Model (PKM) then estimates corresponding multi-view optical flows during the Multi-view Flow Estimation Stage (MFES). Subsequently, these optical flows are utilized to generate multi-view motion results through decoupled motion representation in the Multi-view Motion Diffusion Stage (MMDS). Extensive experiments demonstrate that MotionDiff outperforms other physics-based generative motion editing methods in achieving high-quality multi-view consistent motion results. Notably, MotionDiff does not require retraining, enabling users to conveniently adapt it for various down-stream tasks.

cs.CV↗

arXiv2024

A Novel Energy-Efficient Salicide-Enhanced Tunnel Device Technology Based on 300mm Foundry Platform Towards AIoT Applications

This work demonstrates a novel energy-efficient tunnel FET (TFET)-CMOS hybrid foundry platform for ultralow-power AIoT applications. By utilizing the proposed monolithic integration process, the novel complementary n and p-type Si TFET technology with dopant segregated source junction and self-aligned drain underlap design is successfully integrated into a 300mm CMOS baseline process without CMOS performance penalty and any new materials, experimentally demonstrating the large Ion and record high Ion/Ioff ratio of 10^7 among TFETs by industry-manufacturers. The device performance and variability are also co-optimized for high-volume production. Further circuit-level implementations are presented based on the calibrated compact model. The proposed TFET-CMOS hybrid logic and SRAM topologies show significant energy efficiency improvement with comparable operation speed compared with standard CMOS circuits, indicating its great potential for power-constraint AIoT applications.

cond-mat.mes-hall↗

arXiv2024

Jet mixing enhancement with Bayesian optimization, deep learning, and persistent data topology

We optimize the jet mixing using large eddy simulations (LES) at a Reynolds number of $3000$. Key methodological enablers consist of Bayesian optimization, a surrogate model enhanced by deep learning, and persistent data topology for physical interpretation. The mixing performance is characterized by an equivalent jet radius ($R_{\rm eq}$) derived from the streamwise velocity in a plane located $8$ diameters downstream. The optimization is performed in a 22-dimensional actuation space that comprises most known excitations. The plant benefits from a 22-dimensional actuation space that comprises most known excitations. This search space parameterizes distributed actuation imposed on the bulk flow and at the periphery of the nozzle in the streamwise and radial directions. The momentum flux measures the energy input of the actuation. The optimization quadruples the jet radius $R_{\rm eq}$ with a $7$-armed blooming jet after around $570$ evaluations. The control input requires $2\%$ momentum flux of the main flow, which is one order of magnitude lower than an ad hoc dual-mode excitation. Intriguingly, a pronounced suboptimum in the search space is associated with a double-helix jet, a new flow pattern. This jet pattern results in a mixing improvement comparable to the blooming jet. A state-of-the-art Bayesian optimization converges towards this double helix solution. The learning is accelerated and converges to another better optimum by including surrogate model trained along the optimization. Persistent data topology extracts the global and many local minima in the actuation space. These minima can be identified with flow patterns beneficial to the mixing.

physics.flu-dyn↗

arXiv2024

Experimental jet control with Bayesian optimization and persistent data topology

This study experimentally optimizes the mixing of a turbulent jet at $Re=10000$ with the surrounding air by targeted shear layer actuation. The forcing is composed of superposed harmonic signals of different azimuthal wavenumber $m$ generated by eight loudspeakers circumferentially distributed around the nozzle lip. Amplitudes and frequencies of the individual harmonic contributions serve as optimization parameters and the time-averaged centerline velocity downstream of the potential core is used as a metric for mixing optimization. The actuation is optimized through Bayesian optimization. Three search spaces are explored - axisymmetric forcing, $m=0$, superposed axisymmetric and helical forcing, $m \in \{0,1\}$, and axisymmetric actuation combined with two counter-rotating helical modes, $m \in \{-1,0,1\}$. High-speed PIV is employed to analyze the jet response to the optimized forcing. The optimization processes are analyzed by persistent data topology. In the search space of axisymmetric excitation, the routine identifies an actuation at the natural frequency of the flow to be most efficient, with the centerline velocity being decreased by $15\%$. The optimal solutions in both the two-mode and three-mode search space converge to a similar forcing with one axial and one helical mode combined at a frequency ratio of around $2.3$. Spectral analysis of the PIV images reveals that for the identified optimal forcing frequencies, a non-linear interaction between forced and natural structures in the jet flow is triggered, leading to a reduction in centerline velocity of around $35\%$. The topology of the most complex search space from the discrete data reveals four basins of attractions, classified into three forcing patterns including axisymmetric, axisym.-helical, and axisym.-flapping. Two deep basins are related to the optimal axisym.-helical pattern, and the others are shallower.

physics.flu-dyn↗

arXiv2024

Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach

Fair machine learning aims to prevent discrimination against individuals or sub-populations based on sensitive attributes such as gender and race. In recent years, causal inference methods have been increasingly used in fair machine learning to measure unfairness by causal effects. However, current methods assume that the true causal graph is given, which is often not true in real-world applications. To address this limitation, this paper proposes a framework for achieving causal fairness based on the notion of interventions when the true causal graph is partially known. The proposed approach involves modeling fair prediction using a Partially Directed Acyclic Graph (PDAG), specifically, a class of causal DAGs that can be learned from observational data combined with domain knowledge. The PDAG is used to measure causal fairness, and a constrained optimization problem is formulated to balance between fairness and accuracy. Results on both simulated and real-world datasets demonstrate the effectiveness of this method.

cs.LG↗

arXiv2023

IEEE 802.11be Wi-Fi 7: Feature Summary and Performance Evaluation

As emerging applications demand increasingly higher throughput, IEEE standard 802.11be -- Extremely High Throughput (EHT), also known as Wi-Fi 7, was published on July 22, 2025. It can be used to meet the demand for the throughput of 4K/8K videos up to tens of Gbps and low-latency video applications such as virtual reality (VR) and augmented reality (AR). Wi-Fi 7 not only scales Wi-Fi 6 with doubled bandwidth, but also supports real-time applications, which brings revolutionary changes to Wi-Fi. In this article, we start by introducing the main objectives and timeline of Wi-Fi 7 and then list the latest key techniques which promote the performance improvement of Wi-Fi 7. Finally, we validate the most critical objectives of Wi-Fi 7 -- the potential up to 30 Gbps throughput and lower latency. System-level simulation results suggest that by combining the new techniques, Wi-Fi 7 achieves 30 Gbps throughput and lower latency than Wi-Fi 6.

cs.NI↗

arXiv2021

Stabilization of the fluidic pinball with gradient-enriched machine learning control

We stabilize the flow past a cluster of three rotating cylinders, the fluidic pinball, with automated gradient-enriched machine learning algorithms. The control laws command the rotation speed of each cylinder in an open- and closed-loop manner. These laws are optimized with respect to the average distance from the target steady solution in three successively richer search spaces. First, stabilization is pursued with steady symmetric forcing. Second, we allow for asymmetric steady forcing. And third, we determine an optimal feedback controller employing nine velocity probes downstream. As expected, the control performance increases with every generalization of the search space. Surprisingly, both open- and closed-loop optimal controllers include an asymmetric forcing, which surpasses symmetric forcing. Intriguingly, the best performance is achieved by a combination of phasor control and asymmetric steady forcing. We hypothesize that asymmetric forcing is typical for pitchfork bifurcated dynamics of nominally symmetric configurations. Key enablers are automated machine learning algorithms augmented with gradient search: explorative gradient method for the open-loop parameter optimization and a gradient-enriched machine learning control (gMLC) for the feedback optimization. gMLC learns the control law significantly faster than previously employed genetic programming control. The gMLC source code is freely available online.

physics.flu-dyn↗