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Ao Ma

Ao Ma contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Biological PhysicsBiomoleculesComputer VisionComputation and LanguageInformation Retrieval

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Published work

4 published item(s)

preprint2026arXiv

Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models

Generating realistic and user-preferred advertisements is a key challenge in e-commerce. Existing approaches utilize multiple independent models driven by click-through-rate (CTR) to controllably create attractive image or text advertisements. However, their pipelines lack cross-modal perception and rely on CTR that only reflects average preferences. Therefore, we explore jointly generating personalized image-text advertisements from historical click behaviors. We first design a Unified Advertisement Generative model (Uni-AdGen) that employs a single autoregressive framework to produce both advertising images and texts. By incorporating a foreground perception module and instruction tuning, Uni-AdGen enhances the realism of the generated content. To further personalize advertisements, we equip Uni-AdGen with a coarse-to-fine preference understanding module that effectively captures user interests from noisy multimodal historical behaviors to drive personalized generation. Additionally, we construct the first large-scale Personalized Advertising image-text dataset (PAd1M) and introduce a Product Background Similarity (PBS) metric to facilitate training and evaluation. Extensive experiments show that our method outperforms baselines in general and personalized advertisement generation. Our project is available at https://github.com/JD-GenX/Uni-AdGen.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing

In-context image generation and editing (ICGE) enables users to specify visual concepts through interleaved image-text prompts, demanding precise understanding and faithful execution of user intent. Although recent unified multimodal models exhibit promising understanding capabilities, these strengths often fail to transfer effectively to image generation. We introduce Re-Align, a unified framework that bridges the gap between understanding and generation through structured reasoning-guided alignment. At its core lies the In-Context Chain-of-Thought (IC-CoT), a structured reasoning paradigm that decouples semantic guidance and reference association, providing clear textual target and mitigating confusion among reference images. Furthermore, Re-Align introduces an effective RL training scheme that leverages a surrogate reward to measure the alignment between structured reasoning text and the generated image, thereby improving the model's overall performance on ICGE tasks. Extensive experiments verify that Re-Align outperforms competitive methods of comparable model scale and resources on both in-context image generation and editing tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A novel mechanism for energy activation in biomolecules

An activated process consists of energy activation and barrier crossing; the former is a prerequisite for the latter. Barrier crossing has been studied extensively, but energy activation has been overlooked due to a lack of means to gauge its progress. We define reaction stability as the probability that reactive trajectories pass a vicinity in phase space; it enabled us to analyze energy activation of a biomolecular isomerization. This process follows a mechanism fundamentally different from presumed mechanisms in standard reaction rate theories: it features accumulation of high kinetic energy in reaction coordinates, achieved by precise synergy between them coordinated by momentum space.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Kinetic energy flows in activated dynamics of biomolecules

Protein conformational changes are activated processes essential for protein functions. Activation in a protein differs from activation in a small molecule in that it involves directed and systematic energy flows through preferred channels encoded in the protein structure. Understanding the nature of these energy flow channels and how energy flows through them during activation is critical for understanding protein conformational changes. We recently developed a rigorous statistical mechanical framework for understanding potential energy flows. Here we complete this theoretical framework with a rigorous theory for kinetic energy flows: potential and kinetic energy inter-convert when impressed forces oppose inertial forces whereas kinetic energy transfers directly from one coordinate to another when inertial forces oppose each other. This theory is applied to analyzing a prototypic system for biomolecular conformational dynamics: the isomerization of an alanine dipeptide. Among the two essential energy flow channels for this process, dihedral phi confronts the activation barrier, whereas dihedral theta receives energy from potential energy flows. Intriguingly, theta helps phi to cross the activation barrier by transferring to phi via direct kinetic energy flow all the energy it received: increase in theta caused by potential energy flow converts into increase in phi. As a compensation, theta receives kinetic energy from bond angle alpha via direct mechanism and bond angle beta via indirect mechanism.

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