arXiv subjects

Chenjie Xu

Publications and source records attributed to Chenjie Xu.

4 recordsLinked to original sources

arXiv2026

Enhancing Vision Foundation Models via Multimodal Continual Pre-Training

Vision Foundation Models (VFMs) provide strong visual representations for a wide range of applications. In this work, we enhance prevailing VFMs through multimodal training, allowing them to effectively process visual inputs at varying resolutions while producing visual representations that are better aligned with language representations, regardless of their original pre-training objectives. To this end, we introduce M-CPT, a Multimodal Continual Pre-Training framework designed to improve the understanding capability of pre-trained VFMs while preserving their strong visual representation quality. M-CPT introduces a Continual Position Embedding (CPE) for handling flexible visual resolutions, along with a feature alignment objective that improves the consistency between visual and textual representations during multimodal training. Extensive experiments on leading VFMs, including DINOv2, SigLIP, and AIMv2, demonstrate that M-CPT consistently improves multimodal understanding performance while preserving strong performance on standard vision benchmarks such as classification and segmentation.

cs.CV↗

arXiv2026

Agentic Discovery of Cryomicroneedle Formulations

Cryomicroneedles offer a route to minimally invasive intradermal delivery of living cells, but their cryogenic formulations must reconcile cell protection with constraints on toxicity and device fabrication. Here we report an AI-assisted, closed-loop workflow for cryomicroneedle cryoprotectant discovery that combines literature curation, Gaussian-process surrogate modelling, Bayesian optimization, and sequential wet-lab validation. A curated dataset of 198 mesenchymal stem-cell cryopreservation formulations from 42 studies was converted into 21 ingredient features and used to train an uncertainty-aware literature prior. This model captured moderate structure in the literature data but failed prospectively, motivating iterative wet-lab correction. Across ten validation iterations and 106 wet-lab observations, the model progressively adapted to cryomicroneedle-specific outcomes: batch RMSE decreased from 41.21 to 6.86 percentage points, later-stage rank correlations became consistently positive, and the cumulative wet-lab predicted-versus-measured summary reached $R^2 = 0.942$. The best validated formulation achieved 95.15\% post-thaw viability with low DMSO, ectoin, ethylene glycol, and fetal bovine serum. However, high viability alone did not ensure intact cryomicroneedle formation, highlighting the need for future multi-objective optimization. These results demonstrate that agent-assisted computational infrastructure can make data-efficient formulation discovery more accessible to labs with minimal data expertise in-house. Project code is available at https://github.com/baitmeister/ML-for-CryoMN.

cs.LG↗

arXiv2024

Large-scale road network partitioning: a deep learning method based on convolutional autoencoder model

With the development of urbanization, the scale of urban road network continues to expand, especially in some Asian countries. Short-term traffic state prediction is one of the bases of traffic management and control. Constrained by the space-time cost of computation, the short-term traffic state prediction of large-scale urban road network is difficult. One way to solve this problem is to partition the whole network into multiple sub-networks to predict traffic state separately. In this study, a deep learning method is proposed for road network partitioning. The method mainly includes three steps. First, the daily speed series for roads are encoded into the matrix. Second, a convolutional autoencoder (AE) is built to extract series features and compress data. Third, the spatial hierarchical clustering method with adjacency relationship is applied in the road network. The proposed method was verified by the road network of Shenzhen which contains more than 5000 links. The results show that AE-hierarchical clustering distinguishes the tidal traffic characteristics and reflects the process of congestion propagation. Furthermore, two indicators are designed to make a quantitative comparison with spectral clustering: intra homogeneity increases by about 9% on average while inter heterogeneity about 9.5%. Compared with past methods, the time cost also decreases. The results may suggest ways to improve the management and control of urban road network in other metropolitan cities. The proposed method is expected to be extended to other problems related to large-scale networks.

eess.SY↗

arXiv2019

Contrast of nuclei in stratified squamous epithelium in optical coherence tomography images at 800 nm

Imaging nuclei of keratinocytes in the stratified squamous epithelium has been a subject of intense research since nucleus associated cellular atypia is the key criteria for the screening and diagnosis of epithelial cancers and their precursors. However, keratinocyte nuclei have been reported to be either low scattering or high scattering, so that these inconsistent reports might have led to misinterpretations of optical images, and more importantly, hindered the establishment of optical diagnostic criteria. We disclose that they are generally low scattering in the core using Micro-optical coherence tomography (micro-OCT) of 1.28 um axial resolution in vivo; those previously reported high scattering or bright signals from nuclei are likely from the nucleocytoplasmic boundary, and the low-scattering nuclear cores were missed possibly due to insufficient axial resolutions (about 4 um). It is further demonstrated that the high scattering signals may be associated with flattening of nuclei and cytoplasmic glycogen accumulation, which are valuable cytologic hallmarks of cell maturation.

q-bio.TO↗