arXiv subjects

Keqi Wang

Publications and source records attributed to Keqi Wang.

At least 19 recordsLinked to original sources

arXiv2026

S1-Omni: A Unified Multimodal Reasoning Model for Scientific Understanding, Prediction, and Generation

We present S1-Omni, a unified multimodal reasoning model for scientific understanding, prediction, and generation. AI for Science (AI4S) has advanced significantly through domain-specific models, tool-augmented LLMs, and scientific language models. However, model capabilities remain highly fragmented, limiting the joint modeling of heterogeneous data, scientific laws, and expert knowledge. S1-Omni addresses this gap by consolidating these capabilities into a single, coherent scientific reasoning model. The architecture of S1-Omni is built upon three core components: unified representation of scientific data, natural-world knowledge alignment, and decoding for domain-specific tasks. First, S1-Omni maps natural-language instructions and scientific objects, including CIF, SMILES, protein sequences, spectra, and scientific images, into a shared representation space. Second, it incorporates scientific laws and expert knowledge into data construction and training, enabling the model to reason from scientific evidence. Third, it performs task-specific decoding to support a broad range of applications, including property prediction, spectrum-to-molecular generation, protein site and structure prediction, and scientific image generation and editing. S1-Omni is trained on S1-Omni-Corpus, which covers 200 scientific tasks and contains millions of reasoning samples, and is evaluated on over 60 scientific benchmarks. It outperforms GPT-5.5 and Gemini-3.1-Pro on most benchmarks and matches or surpasses domain-specific models on several benchmarks. Overall, S1-Omni provides a practical path toward unified scientific modeling.

cs.AI↗

arXiv2026

Adaptive Fast-Slow Operator Splitting for Multiscale Biochemical Stochastic Dynamics

Stochastic reaction networks governed by Chemical Langevin Equations (CLE) exhibit pronounced multiscale dynamics spanning fast molecular reactions, intermediate transport, and slow cellular regulation, posing significant challenges for efficient and accurate simulation. Although operator splitting naturally decouples fast and slow subsystems, a rigorous error characterization for CLE splitting schemes has been lacking. We propose a modular operator-splitting framework with adaptive discretization that enables reliable and efficient simulation across fast-slow dynamics with explicit control of discretization error. Using stochastic logarithmic representations, we present a complete error analysis of the fast-slow Lie-Trotter splitting method, decomposing the one-step error into stochastic flow truncation error, commutator errors due to subsystem noncommutativity, and numerical discretization errors from fast and slow integrations. Guided by this analysis, we develop a proportional-integral (PI) adaptive controller that jointly selects macro time steps and fast microsteps, achieving substantial efficiency gains while maintaining accuracy.

math.NA↗

arXiv2026

Intracellular Measurement-Informed Multiscale Modeling for Scalable iPSC Manufacturing

Scalable manufacturing of human induced pluripotent stem cells (iPSCs) is essential for industrial-scale production of cell therapies and regenerative medicines. However, the 3D aggregate cultures used in manufacturing exhibit substantial spatial and metabolic heterogeneity compared with the relatively homogeneous monolayer systems used in laboratory studies, complicating mechanistic understanding and predictive metabolic modeling across culture scales. To address this challenge, we developed a modular multiscale mechanistic foundation model that links molecular, cellular, and macroscopic processes while accounting for spatial and metabolic heterogeneity. The framework integrates extracellular culture dynamics, intracellular metabolic fluxes, and cellular redox states by extending a previously established monolayer kinetic network and coupling it with a biological systems-of-systems (Bio-SoS) multiscale model for aggregate cultures, incorporating explicit redox interactions. Systematic monolayer and aggregate experiments (including multiple isotopic tracers, extracellular metabolite profiling, and two-photon optical redox imaging) were used to improve and validate the model. This integrated framework unifies heterogeneous datasets across culture configurations and enables mechanistic interpretation of metabolic and redox responses across heterogeneous culture scales, providing a quantitative foundation for scalable iPSC biomanufacturing.

q-bio.CB↗

arXiv2026

A Modular Mechanistic In Silico Model for In Vitro Transcription Process Yield and Product Quality Prediction

In vitro transcription (IVT) plays a critical role in the manufacture of mRNA vaccines and therapeutics. Optimizing mRNA yield and ensuring product quality, such as capping efficiency and integrity, are essential but mechanistically complex. This study presents a modular mechanistic model of the IVT process to advance scientific understanding and improve predictive capability. The IVT reaction network is decomposed into interconnected modules describing (1) initiation and capping, (2) elongation and truncation, (3) termination and read-through, (4) mRNA degradation, (5) magnesium pyrophosphate precipitation, and (6) enzymatic degradation of pyrophosphate. Guided by biochemical principles and experimental data, kinetic models were developed for each module, accounting for mass balances, molecular complexation, and enzyme activity, and were subsequently assembled to capture coupled IVT dynamics. Multivariate residual analysis and Shapley value-based sensitivity analysis, guided by domain knowledge, were applied to iteratively improve model fidelity. These machine learning-driven analytics enabled identification of key mechanisms, supported in silico experimentation, and facilitated root-cause analysis. Combined with Gaussian-process-based batch Bayesian optimization for efficient parameter estimation, this framework establishes a scalable hybrid (mechanistic + machine learning) modeling platform that integrates heterogeneous data, accelerates model calibration, and supports rational design and optimization of mRNA manufacturing processes.

q-bio.MN↗

arXiv2025

Multi-Scale Hybrid Modeling to Predict Cell Culture Process with Metabolic Phase Transitions

To advance understanding of cellular metabolism and reduce batch-to-batch variability in cell culture processes, this study introduces a multi-scale hybrid modeling framework designed to simulate and predict the dynamic behavior of CHO cell cultures undergoing metabolic phase transitions. The model captures dependencies across molecular, cellular, and macro-kinetic levels, accounting for variability in single-cell metabolic phases. It integrates three components: (i) a stochastic mechanistic model of single-cell metabolic networks, (ii) a probabilistic model of phase transitions, and (iii) a macro-kinetic model of heterogeneous population dynamics. This modular architecture enables flexible representation of process trajectories under diverse conditions and incorporates heterogeneous online (e.g., oxygen uptake, pH) and offline measurements (e.g., viable cell density, metabolite concentrations). Leveraging these data and single-cell insights, the framework predicts culture dynamics using only readily available online measurements and initial conditions, delivering accurate long-term forecasts of multivariate culture behavior and uncertainty-aware estimates of batch-to-batch variation. Overall, this work establishes a robust foundation for digital twin platforms and predictive bioprocess analytics, supporting systematic experimental design and process control to improve yield and production stability in biomanufacturing.

q-bio.MN↗

arXiv2025

Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding

We introduce Lumina-DiMOO, an open-source foundational model for seamless multi-modal generation and understanding. Lumina-DiMOO sets itself apart from prior unified models by utilizing a fully discrete diffusion modeling to handle inputs and outputs across various modalities. This innovative approach allows Lumina-DiMOO to achieve higher sampling efficiency compared to previous autoregressive (AR) or hybrid AR-Diffusion paradigms and adeptly support a broad spectrum of multi-modal tasks, including text-to-image generation, image-to-image generation (e.g., image editing, subject-driven generation, and image inpainting, etc.), as well as image understanding. Lumina-DiMOO achieves state-of-the-art performance on multiple benchmarks, surpassing existing open-source unified multi-modal models. To foster further advancements in multi-modal and discrete diffusion model research, we release our code and checkpoints to the community. Project Page: https://synbol.github.io/Lumina-DiMOO.

cs.CV↗

arXiv2025

A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example

Bioprocess mechanistic modeling is essential for advancing intelligent digital twin representation of biomanufacturing, yet challenges persist due to complex intracellular regulation, stochastic system behavior, and limited experimental data. This paper introduces a symbolic and statistical learning framework to identify key regulatory mechanisms and quantify model uncertainty. Bioprocess dynamics is formulated with stochastic differential equations characterizing intrinsic process variability, with a predefined set of candidate regulatory mechanisms constructed from biological knowledge. A Bayesian learning approach is developed, which is based on a joint learning of kinetic parameters and regulatory structure through a formulation of the mixture model. To enhance computational efficiency, a Metropolis-adjusted Langevin algorithm with adjoint sensitivity analysis is developed for posterior exploration. Compared to state-of-the-art Bayesian inference approaches, the proposed framework achieves improved sample efficiency and robust model selection. An empirical study demonstrates its ability to recover missing regulatory mechanisms and improve model fidelity under data-limited conditions.

stat.ML↗

arXiv2024

Charge Collection Performance of 4H-SiC LGAD

The 4H-SiC material exhibits good detection performance, but there are still many problems like signal distortion and poor signal quality. The 4H-SiC low gain avalanche detector (LGAD) has been fabricated for the first time to solve these problems, which named SICAR (SIlicon CARbide). The results of electrical characteristics and charge collection performance of the 4H-SiC LGAD are reported. The influence of different metal thicknesses on the leakage current of the device is studied.~By optimizing the fabrication process, the leakage current of the detector is reduced by four orders of magnitude. The experimental results confirm this 4H-SiC LGAD has an obvious gain structure, the gain factor of the SICAR is reported to be about 2 at 150 V. The charge collection efficiency (CCE) of the device was analyzed using $α$ particle incidence of 5.54 MeV, and the CCE is 90\% @100~V. This study provides a novel 4H-SiC LGAD radiation detector for application in the field of high energy particle physics.

physics.ins-det↗

arXiv2024

High precision proton beam monitor system concept design on CSNS based on SiC

A high precision beam monitor system based on silicon carbide PIN sensor is designed for China Spallation Neutron Source 1.6 GeV proton beam to monitor the proton beam fluence.The concept design of the beam monitor system is finished together with front-end electronics with silicon carbide PIN sensors, readout system and mechanical system.Several tests are performed to study the performance of each component of the system.The charge collection of the SiC PIN sensors after proton radiation is studied with 80 MeV proton beam for continuous running. Research on the performance of the front-end electronics and readout system is finished for better data acquisition.The uncertainty of proton beam fluence is below 1% in the beam monitor system.

physics.acc-ph↗

arXiv2023

Metabolic Regulatory Network Kinetic Modeling with Multiple Isotopic Tracers for iPSCs

The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies.

q-bio.MN↗

arXiv2023

Stochastic Molecular Reaction Queueing Network Modeling for In Vitro Transcription Process

To facilitate a rapid response to pandemic threats, this paper focuses on developing a mechanistic simulation model for in vitro transcription (IVT) process, a crucial step in mRNA vaccine manufacturing. To enhance production and support industry 4.0, this model is proposed to improve the prediction and analysis of IVT enzymatic reaction network. It incorporates a novel stochastic molecular reaction queueing network with a regulatory kinetic model characterizing the effect of bioprocess state variables on reaction rates. The empirical study demonstrates that the proposed model has a promising performance under different production conditions and it could offer potential improvements in mRNA product quality and yield.

q-bio.MN↗

arXiv2023

Design and simulation of a novel 4H-SiC LGAD timing device

Silicon-based fast time detectors have been widely used in high energy physics, nuclear physics, space exploration and other fields in recent years. However, silicon detectors often require complex low-temperature systems when operating in irradiation environment, and their detection performance decrease with the increase of irradiation dose. Compared with silicon, silicon carbide (SiC) has a wider bandgap, higher atomic displacement energy, saturated electron drift velocity and thermal conductivity. Simultaneously, the low gain avalanche detector avoids crosstalk and high noise from high multiplication due to its moderate gain, and thus can maintain a high detector signal without increasing noise. Thus, the 4H-SiC particle detector, especially the low gain avalanche detector has the potential to detect the minimal ionized particles (MIPs) under extreme irradiation and high temperature environments. In this work, the emphasis was placed on the design of a 4H-SiC Low Gain Avalanche Detector (LGAD), especially the epitaxial structure and technical process which played the main roles. In addition, a simulation tool--RASER(RAdiation SEmiconductoR) was developed to simulate the performances including the electrical properties and time resolution of the 4H-SiC LGAD we proposed. The working voltage and gain effectiveness of the LGAD were verified by the simulation of electrical performances. The time resolution of the LGAD is (35.0 $\pm$ 0.2) ps under the electrical field of -800 V, which is better than that of the 4H-SiC PIN detector.

physics.ins-det↗

arXiv2022

Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control

Driven by the critical needs of biomanufacturing 4.0, we introduce a probabilistic knowledge graph hybrid model characterizing the risk- and science-based understanding of bioprocess mechanisms. It can faithfully capture the important properties, including nonlinear reactions, partially observed state, and nonstationary dynamics. Given very limited real process observations, we derive a posterior distribution quantifying model estimation uncertainty. To avoid the evaluation of intractable likelihoods, Approximate Bayesian Computation sampling with Sequential Monte Carlo (ABC-SMC) is utilized to approximate the posterior distribution. Under high stochastic and model uncertainties, it is computationally expensive to match output trajectories. Therefore, we create a linear Gaussian dynamic Bayesian network (LG-DBN) auxiliary likelihood-based ABC-SMC approach. Through matching the summary statistics driven through LG-DBN likelihood that can capture critical interactions and variations, the proposed algorithm can accelerate hybrid model inference, support process monitoring, and facilitate mechanism learning and robust control.

stat.ML↗

arXiv2022

Stochastic Simulation Uncertainty Analysis to Accelerate Flexible Biomanufacturing Process Development

Motivated by critical challenges and needs from biopharmaceuticals manufacturing, we propose a general metamodel-assisted stochastic simulation uncertainty analysis framework to accelerate the development of a simulation model with modular design for flexible production processes. There are often very limited process observations. Thus, there exist both simulation and model uncertainties in the system performance estimates. In biopharmaceutical manufacturing, model uncertainty often dominates. The proposed framework can produce a confidence interval that accounts for simulation and model uncertainties by using a metamodel-assisted bootstrapping approach. Furthermore, a variance decomposition is utilized to estimate the relative contributions from each source of model uncertainty, as well as simulation uncertainty. This information can be used to improve the system mean performance estimation. Asymptotic analysis provides theoretical support for our approach, while the empirical study demonstrates that it has good finite-sample performance.

stat.ME↗

arXiv2022

Linear Array Network for Low-light Image Enhancement

Convolution neural networks (CNNs) based methods have dominated the low-light image enhancement tasks due to their outstanding performance. However, the convolution operation is based on a local sliding window mechanism, which is difficult to construct the long-range dependencies of the feature maps. Meanwhile, the self-attention based global relationship aggregation methods have been widely used in computer vision, but these methods are difficult to handle high-resolution images because of the high computational complexity. To solve this problem, this paper proposes a Linear Array Self-attention (LASA) mechanism, which uses only two 2-D feature encodings to construct 3-D global weights and then refines feature maps generated by convolution layers. Based on LASA, Linear Array Network (LAN) is proposed, which is superior to the existing state-of-the-art (SOTA) methods in both RGB and RAW based low-light enhancement tasks with a smaller amount of parameters. The code is released in https://github.com/cuiziteng/LASA_enhancement.

cs.CV↗

arXiv2022

Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Control

Driven by the key challenges of cell therapy manufacturing, including high complexity, high uncertainty, and very limited process observations, we propose a hybrid model-based reinforcement learning (RL) to efficiently guide process control. We first create a probabilistic knowledge graph (KG) hybrid model characterizing the risk- and science-based understanding of biomanufacturing process mechanisms and quantifying inherent stochasticity, e.g., batch-to-batch variation. It can capture the key features, including nonlinear reactions, nonstationary dynamics, and partially observed state. This hybrid model can leverage existing mechanistic models and facilitate learning from heterogeneous process data. A computational sampling approach is used to generate posterior samples quantifying model uncertainty. Then, we introduce hybrid model-based Bayesian RL, accounting for both inherent stochasticity and model uncertainty, to guide optimal, robust, and interpretable dynamic decision making. Cell therapy manufacturing examples are used to empirically demonstrate that the proposed framework can outperform the classical deterministic mechanistic model assisted process optimization.

eess.SY↗

arXiv2021

Blockchain-Enabled Internet-of-Things Platform for End-to-End Industrial Hemp Supply Chain

After being legalized as an agricultural commodity by the 2018 U.S. Farm Bill, the Industrial Hemp production is moved from limited pilot programs to a regulated agriculture production system, and the market keeps increasing since then. However, Industrial Hemp Supply Chain (IHSC) faces several critical challenges, including high complexity and variability, data tampering, and lack of immutable information tracking system. In this paper, we develop a blockchain enabled internet-of-things (IoT) platform for IHSC to support process tracking, scalability, interoperability, and risk management. Basically, we create a two-layer blockchain with proof-of-authority based smart contract, which can leverage local authorities with state/federal regulators to ensure and accelerate quality control verification and regulatory compliance. Then, we develop a user-friendly mobile app so that each participant can use smart phone to real-time collect and upload their data to the cloud, and further share the process verification and tracking information through the blockchain network. Our study indicates the proposed platform can support interoperability, improve the efficiency of quality control verification, and ensure the safety of regulated IHSC.

cs.CR↗

arXiv2020

Data-Driven Stochastic Optimization for Power Grids Scheduling under High Wind Penetration

To address the environmental concern and improve the economic efficiency, the wind power is rapidly integrated into smart grids. However, the inherent uncertainty of wind energy raises operational challenges. To ensure the cost-efficient, reliable and robust operation, it is critically important to find the optimal decision that can correctly and rigorously hedge against all sources of uncertainty. In this paper, we propose data-driven stochastic unit commitment (SUC) to guide the power grids scheduling. Specifically, given the finite historical data, the posterior predictive distribution is developed to quantify the wind power prediction uncertainty accounting for both inherent stochastic uncertainty of wind power generation and input model estimation error. For complex power grid systems, a finite number of scenarios is used to estimate the expected cost in the planning horizon. To further control the impact of finite sampling error induced by using the sample average approximation (SAA), we propose a parallel computing based optimization solution methodology, which can quickly find the reliable optimal unit commitment decision hedging against various sources of uncertainty. The empirical study over six-bus and 118-bus systems demonstrates that our approach can provide more efficient and robust performance than the existing deterministic and stochastic unit commitment approaches.

eess.SY↗