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Yulin Li

Yulin Li appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
physics.acc-phComputer VisionArtificial Intelligencecond-mat.softMachine LearningRobotics

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Published work

10 published item(s)

preprint2026arXiv

AgentSteerTTS: A Multi-Agent Closed-Loop Framework for Composite-Instruction Text-to-Speech

While existing text-to-speech (TTS) models exhibit high expressiveness, fine-grained control over composite instructions remains challenging due to the structural mismatch between discrete textual intents and continuous acoustic realizations. Inspired by human cognitive decoupling, we introduce AgentSteerTTS, a multi-agent closed-loop framework designed for intent-faithful expressive control of composite instructions. First, in our framework, an adversarial disentanglement agent mitigates speaker-emotion leakage by learning separable identity and emotion-prosody subspaces with leakage-suppressing regularization. Next, a Dual-Stream Anchoring Controller grounds abstract intents using a large-scale acoustic prototype library: a Retrieval Agent selects expressive anchors, while a Synthesis Agent fuses them into continuous control vectors via gated attention. Finally, a Fast-Slow Feedback Agent refines output intensity through latent gradient correction and resolves semantic-acoustic mismatches using high-level perceptual critique. Experiments on a composite-instruction benchmark and public test sets show that AgentSteerTTS yields consistent and significant improvements to the baselines, demonstrating the effectiveness of the proposed method.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Benchmarking and Evolving Reason-Reflect-Rectify for Reflective Visual Generation

Text-to-Image (T2I) models and Unified Multimodal Models (UMMs) have achieved remarkable progress in visual generation. However, their reliance on a single-pass generation paradigm limits their ability to handle complex prompts requiring iterative refinement. To enable multi-round Reflective Visual Generation (RVG), we formalize the Reason-Reflect-Rectify (R^3) loop as a core framework and introduce R^3-Bench, a benchmark of over 600 expert-annotated instances that quantifies iterative reasoning and rectification capabilities. Evaluation on R^3-Bench reveals a critical gap: while state-of-the-art models can identify generation errors, they fail to generate actionable rectification instructions. To bridge this gap, we propose R^3-Refiner, a dual-stage framework leveraging Group Relative Policy Optimization (GRPO) and a Hierarchical Reward Mechanism (HRM) to better align rectification with reflective reasoning. Experiments show that R^3-Refiner achieves significant improvements on R^3-Bench (+12.0% in Reflective Verdict Score, +9.0% in Rectification Score), and can be seamlessly integrated with various MLLMs to enhance the generation quality of different T2I models on GenEval++ and T2I-CompBench. Code is available at https://github.com/xiaomoguhz/R3-Bench.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Collective bacterial motion drives interfacial waves and shape dynamics in phase-separated droplets

Liquid-liquid phase separation is important across biology, physics, and materials science. Although usually studied at equilibrium, active components - such as motor proteins, enzymes, and synthetic microswimmers - are increasingly recognized as key players in reshaping phase separation dynamics. Here, we encapsulate motile bacteria inside phase-separated aqueous droplets to investigate how internal activity alters interfacial behavior. By varying bacterial density, we control the active stress at the droplet interface. At low activity, we observe scale-dependent interfacial fluctuations that propagate as waves. In this low Reynolds number regime, these waves arise from an effective inertial response, generated when active bacterial stresses balance passive viscous damping of the interface. At higher activity, droplets deform strongly - exceeding the Plateau-Rayleigh instability threshold - and even form cell-sized filaments - a morphology without a passive counterpart. Enhanced droplet motility and accelerated coarsening accompany these shape changes. Our work shows how active stresses can reshape the morphology and dynamics of multiphase systems, offering new insight into the physics of internally driven phase-separated fluids.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Online Trajectory Optimization for Arbitrary-Shaped Mobile Robots via Polynomial Separating Hypersurfaces

An emerging class of trajectory optimization methods enforces collision avoidance by jointly optimizing the robot's configuration and a separating hyperplane. However, as linear separators only apply to convex sets, these methods require convex approximations of both the robot and obstacles, which becomes an overly conservative assumption in cluttered and narrow environments. In this work, we unequivocally remove this limitation by introducing nonlinear separating hypersurfaces parameterized by polynomial functions. We first generalize the classical separating hyperplane theorem and prove that any two disjoint bounded closed sets in Euclidean space can be separated by a polynomial hypersurface, serving as the theoretical foundation for nonlinear separation of arbitrary geometries. Building on this result, we formulate a nonlinear programming (NLP) problem that jointly optimizes the robot's trajectory and the coefficients of the separating polynomials, enabling geometry-aware collision avoidance without conservative convex simplifications. The optimization remains efficiently solvable using standard NLP solvers. Simulation and real-world experiments with nonconvex robots demonstrate that our method achieves smooth, collision-free, and agile maneuvers in environments where convex-approximation baselines fail.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Frequency Domain Modality-invariant Feature Learning for Visible-infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is challenging due to the significant cross-modality discrepancies between visible and infrared images. While existing methods have focused on designing complex network architectures or using metric learning constraints to learn modality-invariant features, they often overlook which specific component of the image causes the modality discrepancy problem. In this paper, we first reveal that the difference in the amplitude component of visible and infrared images is the primary factor that causes the modality discrepancy and further propose a novel Frequency Domain modality-invariant feature learning framework (FDMNet) to reduce modality discrepancy from the frequency domain perspective. Our framework introduces two novel modules, namely the Instance-Adaptive Amplitude Filter (IAF) module and the Phrase-Preserving Normalization (PPNorm) module, to enhance the modality-invariant amplitude component and suppress the modality-specific component at both the image- and feature-levels. Extensive experimental results on two standard benchmarks, SYSU-MM01 and RegDB, demonstrate the superior performance of our FDMNet against state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Neural-iLQR: A Learning-Aided Shooting Method for Trajectory Optimization

Iterative linear quadratic regulator (iLQR) has gained wide popularity in addressing trajectory optimization problems with nonlinear system models. However, as a model-based shooting method, it relies heavily on an accurate system model to update the optimal control actions and the trajectory determined with forward integration, thus becoming vulnerable to inevitable model inaccuracies. Recently, substantial research efforts in learning-based methods for optimal control problems have been progressing significantly in addressing unknown system models, particularly when the system has complex interactions with the environment. Yet a deep neural network is normally required to fit substantial scale of sampling data. In this work, we present Neural-iLQR, a learning-aided shooting method over the unconstrained control space, in which a neural network with a simple structure is used to represent the local system model. In this framework, the trajectory optimization task is achieved with simultaneous refinement of the optimal policy and the neural network iteratively, without relying on the prior knowledge of the system model. Through comprehensive evaluations on two illustrative control tasks, the proposed method is shown to outperform the conventional iLQR significantly in the presence of inaccuracies in system models.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Shielded button electrodes for time-resolved measurements of electron cloud buildup

We report on the design, deployment and signal analysis for shielded button electrodes sensitive to electron cloud buildup at the Cornell Electron Storage Ring. These simple detectors, derived from a beam-position monitor electrode design, have provided detailed information on the physical processes underlying the local production and lifetime of electron densities in the storage ring. Digitizing oscilloscopes are used to record electron fluxes incident on the vacuum chamber wall in 1024 time steps of 100 ps or more. The fine time steps provide a detailed characterization of the cloud, allowing the independent estimation of processes contributing on differing time scales and providing sensitivity to the characteristic kinetic energies of the electrons making up the cloud. By varying the spacing and population of electron and positron beam bunches, we map the time development of the various cloud production and re-absorption processes. The excellent reproducibility of the measurements also permits the measurement of long-term conditioning of vacuum chamber surfaces.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Demonstration of Low Emittance in the Cornell Energy Recovery Linac Injector Prototype

We present a detailed study of the six-dimensional phase space of the electron beam produced by the Cornell Energy Recovery Linac Photoinjector, a high-brightness, high repetition rate (1.3 GHz) DC photoemission source designed to drive a hard x-ray energy recovery linac (ERL). A complete simulation model of the injector has been constructed, verified by measurement, and optimized. Both the horizontal and vertical 2D transverse phase spaces, as well as the time-resolved (sliced) horizontal phase space, were simulated and directly measured at the end of the injector for 19 pC and 77 pC bunches at roughly 8 MeV. These bunch charges were chosen because they correspond to 25 mA and 100 mA average current if operating at the full 1.3 GHz repetition rate. The resulting 90% normalized transverse emittances for 19 (77) pC/bunch were 0.23 +/- 0.02 (0.51 +/- 0.04) microns in the horizontal plane, and 0.14 +/- 0.01 (0.29 +/- 0.02) microns in the vertical plane, respectively. These emittances were measured with a corresponding bunch length of 2.1 +/- 0.1 (3.0 +/- 0.2) ps, respectively. In each case the rms momentum spread was determined to be on the order of 1e-3. Excellent overall agreement between measurement and simulation has been demonstrated. Using the emittances and bunch length measured at 19 pC/bunch, we estimate the electron beam quality in a 1.3 GHz, 5 GeV hard x-ray ERL to be at least a factor of 20 times better than that of existing storage rings when the rms energy spread of each device is considered. These results represent a milestone for the field of high-brightness, high-current photoinjectors.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Photocathode Behavior During High Current Running in the Cornell ERL Photoinjector

The Cornell University Energy Recovery Linac (ERL) photoinjector has recently demonstrated operation at 20 mA for approximately 8 hours, utilizing a multialkali photocathode deposited on a Si substrate. We describe the recipe for photocathode deposition, and will detail the parameters of the run. Post-run analysis of the photocathode indicates the presence of significant damage to the substrate, perhaps due to ion back-bombardment from the residual beamline gas. While the exact cause of the substrate damage remains unknown, we describe multiple surface characterization techniques (X-ray fluorescence spectroscopy, X-ray diffraction, atomic force and scanning electron microscopy) used to study the interesting morphological and crystallographic features of the photocathode surface after its use for high current beam production. Finally, we present a simple model of crystal damage due to ion back-bombardment, which agrees qualitatively with the distribution of damage on the substrate surface.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Thermal emittance measurements of a cesium potassium antimonide photocathode

Thermal emittance measurements of a CsK2Sb photocathode at several laser wavelengths are presented. The emittance is obtained with a solenoid scan technique using a high voltage dc photoemission gun. The thermal emittance is 0.56+/-0.03 mm-mrad/mm(rms) at 532 nm wavelength. The results are compared with a simple photoemission model and found to be in a good agreement.

0 citations0 reviewsNo code linkedOpen access