Zhe Jia

Zhe Jia, Xiaotian Zhang, Junpeng Li

Inferring high-dimensional physical states from sparse, ad-hoc sensor arrays is a fundamental challenge across AI for Science, yet standard architectures like CNNs and DeepSets struggle to capture the irregular geometries and relational physics inherent to domains like seismology. To address this, we propose SourceNet, a Transformer-based framework that bridges the profound Sim-to-Real gap via Physics-Structured Domain Randomization (PSDR), a protocol that randomizes governing physical dynamics to enforce invariance to unmodeled environmental heterogeneity. By pre-training on 100,000 synthetic events and fine-tuning on ~2,500 real-world events, SourceNet achieves state-of-the-art precision on held-out real data, demonstrating exceptional data efficiency and real-time capability compared to classical solvers. Beyond prediction, interpretability analysis reveals that the model shows scientific-agent-like features: it autonomously discovers geometric information bottlenecks and learns an attention policy that prioritizes sparse sensor placements, effectively recovering principles of optimal experimental design from data alone.

Chengyin Hu, Xiang Chen, Zhe Jia et al.

Vision-Language Models (VLMs) are trained on image-text pairs collected under canonical visual conditions and achieve strong performance on multimodal tasks. However, their robustness to real-world weather conditions, and the stability of cross-modal semantic alignment under such structured perturbations, remain insufficiently studied. In this paper, we focus on rainy scenarios and introduce the first adversarial framework that exploits realistic weather to attack VLMs, using a two-stage, parameterized perturbation model based on semantic decoupling to analyze rain-induced shifts in decision-making. In Stage 1, we model the global effects of rainfall by applying a low-dimensional global modulation to condition the embedding space and gradually weaken the original semantic decision boundaries. In Stage 2, we introduce structured rain variations by explicitly modeling multi-scale raindrop appearance and rainfall-induced illumination changes, and optimize the resulting non-differentiable weather space to induce stable semantic shifts. Operating in a non-pixel parameter space, our framework generates perturbations that are both physically grounded and interpretable. Experiments across multiple tasks show that even physically plausible, highly constrained weather perturbations can induce substantial semantic misalignment in mainstream VLMs, posing potential safety and reliability risks in real-world deployment. Ablations further confirm that illumination modeling and multi-scale raindrop structures are key drivers of these semantic shifts.

Xiaotian Zhang, Zhe Jia, Zachary E. Ross et al.

We present a machine-learning approach to classifying the phases of surface wave dispersion curves. Standard FTAN analysis of surfaces observed on an array of receivers is converted to an image, of which, each pixel is classified as fundamental mode, first overtone, or noise. We use a convolutional neural network (U-net) architecture with a supervised learning objective and incorporate transfer learning. The training is initially performed with synthetic data to learn coarse structure, followed by fine-tuning of the network using approximately 10% of the real data based on human classification. The results show that the machine classification is nearly identical to the human picked phases. Expanding the method to process multiple images at once did not improve the performance. The developed technique will faciliate automated processing of large dispersion curve datasets.

Yu Fu, Zhe Jia, Lu Yu et al.

Both enterprise and national firewalls filter network connections. For data forensics and botnet removal applications, it is important to establish the information source. In this paper, we describe a data transport layer which allows a client to transfer encrypted data that provides no discernible information regarding the data source. We use a domain generation algorithm (DGA) to encode AES encrypted data into domain names that current tools are unable to reliably differentiate from valid domain names. The domain names are registered using (free) dynamic DNS services. The data transmission format is not vulnerable to Deep Packet Inspection (DPI).