martin renqiang min Archives | NEC Labs America

Identifying Combinatorial Regulatory Genes for Cell Fate Decision via Reparameterizable Subset Explanations

August 3, 2025/in Publications/by NEC Labs America

Cell fate decisions are highly coordinated processes governed bycomplex interactions among numerous regulatory genes, whiledisruptions in these mechanisms can lead to developmental abnormalitiesand disease. Traditional methods often fail to capture suchcombinatorial interactions, limiting their ability to fully model cellfate dynamics. Here, we introduce MetaVelo, a global feature explanationframework for identifying key regulatory gene sets influencingcell fate transitions. MetaVelo models these transitions as ablack-box function and employs a differentiable neural ordinary differentialequation (ODE) surrogate to enable efficient optimization.By reparameterizing the problem as a controllable data generationprocess, MetaVelo overcomes the challenges posed by the nondifferentiablenature of cell fate dynamics. Benchmarking acrossdiverse stand-alone and longitudinal single-cell RNA-seq datasetsand three black-box cell fate models demonstrates its superiorityover 12 baseline methods in predicting developmental trajectoriesand identifying combinatorial regulatory gene sets. MetaVelo furtherdistinguishes independent from synergistic regulatory genes,offering novel insights into the gene interactions governing cellfate. With the growing availability of high-resolution single-celldata, MetaVelo provides a scalable and effective framework fo

Group Relative Augmentation for Data Efficient Action Detection

July 30, 2025/in Publications/by NEC Labs America

Adapting large Video-Language Models (VLMs) for action detection using only a few examples poses challenges like overfitting and the granularity mismatch between scene-level pre-training and required person-centric understanding. We propose an efficient adaptation strategy combining parameter-efficient tuning (LoRA) with a novel learnable internal feature augmentation. Applied within the frozen VLM backbone using FiLM, these augmentations generate diverse feature variations directly relevant to the task. Additionally, we introduce a group-weighted loss function that dynamically modulates the training contribution of each augmented sample based on its prediction divergence relative to the group average. This promotes robust learning by prioritizing informative yet reasonable augmentations. We demonstrate our method’s effectiveness on complex multi-label, multi-person action detection datasets (AVA, MOMA), achieving strong mAP performance and showcasing significant data efficiency for adapting VLMs from limited examples.

PPDiff: Diffusing in Hybrid Sequence-Structure Space for Protein-Protein Complex Design

July 13, 2025/in Publications/by NEC Labs America

Designing protein-binding proteins with high affinity is critical in biomedical research and biotechnology. Despite recent advancements targeting specific proteins, the ability to create high-affinity binders for arbitrary protein targets on demand, without extensive rounds of wet-lab testing,remains a significant challenge. Here, we introduce PPDiff, a diffusion model to jointly design the sequence and structure of binders for arbitrary protein targets in a non-autoregressive manner. PPDiff builds upon our developed Sequence Structure Interleaving Network with Causal attention layers (SSINC), which integrates interleaved self-attention layers to capture global amino acid correlations, k-nearest neighbor (kNN) equivariant graph layers to model local interactions in three-dimensional (3D) space, and causal attention layers to simplify the intricate interdependencies within the protein sequence. To assess PPDiff, we curate PPBench, a general protein complex dataset comprising 706,360 complexes from the Protein Data Bank (PDB). The model is pretrained on PPBench and finetuned on two real-world applications: target-protein mini-binder complex design and antigen-antibody complex design. PPDiff consistently surpasses baseline methods, achieving success rates of 50.00%, 23.16%, and 16.89% for the pretraining task and the two downstream applications, respectively.

Solving Inverse Problems via a Score-Based Prior: An Approximation-Free Posterior Sampling Approach

June 5, 2025/in Publications/by NEC Labs America

Diffusion models (DMs) have proven to be effective in modeling high-dimensional distributions, leading to their widespread adoption for representing complex priors in Bayesian inverse problems (BIPs). However, current DM-based posterior sampling methods proposed for solving common BIPs rely on heuristic approximations to the generative process. To exploit the generative capability of DMs and avoid the usage of such approximations, we propose an ensemble-based algorithm that performs posterior sampling without the use of heuristic approximations. Our algorithm is motivated by existing works that combine DM-based methods with the sequential Monte Carlo (SMC) method. By examining how the prior evolves through the diffusion process encoded by the pre-trained score function, we derive a modified partial differential equation (PDE) governing the evolution of the corresponding posterior distribution. This PDE includes a modified diffusion term and a reweighting term, which can be simulated via stochastic weighted particle methods. Theoretically, we prove that the error between the true posterior distribution canbe bounded in terms of the training error of the pre-trained score function and the ]number of particles in the ensemble. Empirically, we validate our algorithm on several inverse problems in imaging to show that our method gives more accurate reconstructions compared to existing DM-based methods.

Attribute-Centric Compositional Text-to-Image Generation

March 13, 2025/in Publications/by NEC Labs America

Despite the recent impressive breakthroughs in text-to-image generation, generative models have difficulty in capturing thedata distribution of underrepresented attribute compositions while over-memorizing overrepresented attribute compositions,which raises public concerns about their robustness and fairness. To tackle this challenge, we propose ACTIG, an attributecentriccompositional text-to-image generation framework. We present an attribute-centric feature augmentation and a novelimage-free training scheme, which greatly improves models ability to generate images with underrepresented attributes.Wefurther propose an attribute-centric contrastive loss to avoid overfitting to overrepresented attribute compositions.We validateour framework on the CelebA-HQ and CUB datasets. Extensive experiments show that the compositional generalization ofACTIG is outstanding, and our framework outperforms previous works in terms of image quality and text-image consistency

Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation

March 4, 2025/in Publications/by NEC Labs America

We consider the conditional generation of 3D drug-like molecules with explicit control over molecular properties such as drug-like properties (e.g., Quantitative Estimate of Druglikenessor Synthetic Accessibility score) and effectively binding to specific protein sites. To tackle this problem, we propose an E(3)-equivariant Wasserstein autoencoder and factorize thelatent space of our generative model into two disentangled aspects: molecular properties and the remaining structural context of 3D molecules. Our model ensures explicit control over these molecular attributes while maintaining equivariance of coordinate representation and invariance of data likelihood. Furthermore, we introduce a novel alignment-based coordinate loss to adapt equivariant networks for auto-regressive denovo 3D molecule generation from scratch. Extensive experiments validate our models effectiveness on property-guidedand context-guided molecule generation, both for de-novo 3D molecule design and structure-based drug discovery against protein targets.

NEC Labs America Attends the 39th Annual AAAI Conference on Artificial Intelligence #AAAI25

February 27, 2025/in Events/by NEC Labs America

Our NEC Lab America team attended the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025. The purpose of the AAAI conference series was to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. Our team presented technical papers, led special tracks, delivered talks on key topics, participated in workshops, conducted tutorials, and showcased research in poster sessions. The team greeted visitors at Booth #208 and was there Thursday through Saturday.

Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation

February 25, 2025/in Publications/by NEC Labs America

Multimodal Large Language Models (MLLMs) have shown impressive performance in vision and text tasks. However, hallucination remains a major challenge, especially in fields like healthcare where details are critical. In this work, we show how MLLMs may be enhanced to support Visual RAG (V-RAG), a retrieval-augmented generation framework that incorporates both text and visual data from retrieved images. On the MIMIC-CXR chest X-ray report generation and Multicare medical image caption generation datasets, we show that Visual RAG improves the accuracy of entity probing, which asks whether a medical entities is grounded by an image. We show that the improvements extend both to frequent and rare entities, the latter of which may have less positive training data. Downstream, we apply V-RAG with entity probing to correct hallucinations and generate more clinically accurate X-ray reports, obtaining a higher RadGraph-F1 score.

Discrete-Continuous Variational Optimization with Local Gradients

December 15, 2024/in Publications/by NEC Labs America

Variational optimization (VO) offers a general approach for handling objectives which may involve discontinuities, or whose gradients are difficult to calculate. By introducing a variational distribution over the parameter space, such objectives are smoothed, and rendered amenable to VO methods. Local gradient information, though, may be available in certain problems, which is neglected by such an approach. We therefore consider a general method for incorporating local information via an augmented VO objective function to accelerate convergence and improve accuracy. We show how our augmented objective can be viewed as an instance of multilevel optimization. Finally, we show our method can train a genetic algorithm simulator, using a recursive Wasserstein distance objective

Understanding Transcriptional Regulatory Redundancy by Learnable Global Subset Perturbations

December 5, 2024/in Publications/by NEC Labs America

Transcriptional regulation through cis-regulatory elements (CREs) is crucial for numerous biological functions, with its disruption potentially leading to various diseases. It is well-known that these CREs often exhibit redundancy, allowing them to compensate for each other in response to external disturbances, highlighting the need for methods to identify CRE sets that collaboratively regulate gene expression effectively. To address this, we introduce GRIDS, an in silico computational method that approaches the task as a global feature explanation challenge to dissect combinatorial CRE effects in two phases. First, GRIDS constructs a differentiable surrogate function to mirror the complex gene regulatory process, facilitating cross-translations in single-cell modalities. It then employs learnable perturbations within a state transition framework to offer global explanations, efficiently navigating the combinatorial feature landscape. Through comprehensive bench marks, GRIDS demonstrates superior explanatory capabilities compared to other leading methods. Moreover, GRIDS s global explanations reveal intricate regulatory redundancy across cell types and states, underscoring its potential to advance our understanding ofcellular regulation in biological research.