arXiv subjects

Jia Tan

Publications and source records attributed to Jia Tan.

4 recordsLinked to original sources

arXiv2025

ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizer

Patient face images provide a convenient mean for evaluating eye diseases, while also raising privacy concerns. Here, we introduce ROFI, a deep learning-based privacy protection framework for ophthalmology. Using weakly supervised learning and neural identity translation, ROFI anonymizes facial features while retaining disease features (over 98\% accuracy, $\kappa > 0.90$). It achieves 100\% diagnostic sensitivity and high agreement ($\kappa > 0.90$) across eleven eye diseases in three cohorts, anonymizing over 95\% of images. ROFI works with AI systems, maintaining original diagnoses ($\kappa > 0.80$), and supports secure image reversal (over 98\% similarity), enabling audits and long-term care. These results show ROFI's effectiveness of protecting patient privacy in the digital medicine era.

cs.CV↗

arXiv2023

Restoring Strong Cosmic Censorship in Reissner-Nordstrom-de Sitter Black Holes via Non-Minimal Electromagnetic-Scalar Couplings

We investigate whether the Strong Cosmic Censorship (SCC) Conjecture can be reinstated in Reissner-Nordstr\"om-de Sitter (RNdS) black holes by introducing non-minimal couplings between the electromagnetic and scalar fields in Einstein-Maxwell-scalar (EMS) theory. By conducting numerical calculations, we find that the SCC can be restored within a specific range of the coupling constant. Notably, for a given value of the cosmological constant, there exists a critical coupling constant above which the EMS theory satisfies the SCC. These findings suggest that the non-minimal couplings between the electromagnetic and scalar fields may play a crucial role in the restoration of the SCC in RNdS spacetime.

gr-qc↗

arXiv2022

Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples

Deep learning is applied to many complex tasks in the field of wireless communication, such as modulation recognition of spectrum waveforms, because of its convenience and efficiency. This leads to the problem of a malicious third party using a deep learning model to easily recognize the modulation format of the transmitted waveform. Some existing works address this problem directly using the concept of adversarial examples in the image domain without fully considering the characteristics of the waveform transmission in the physical world. Therefore, we propose a low-intercept waveform~(LIW) generation method that can reduce the probability of the modulation being recognized by a third party without affecting the reliable communication of the friendly party. Our LIW exhibits significant low-interception performance even in the physical hardware experiment, decreasing the accuracy of the state of the art model to approximately $15\%$ with small perturbations.

cs.LG↗

arXiv2019

Photoelectron holographic interferometry to probe the longitudinal momentum offset at the tunnel exit

Laser-induced electron tunneling underlies numerous emerging spectroscopic techniques to probe attosecond electron dynamics in atoms and molecules. The improvement of those techniques requires an accurate knowledge of the exit momentum for the tunneling wave packet. Here we demonstrate a photoelectron interferometric scheme to probe the electron momentum longitudinal to the tunnel direction at the tunnel exit by measuring the photoelectron holographic pattern in an orthogonally polarized two-color laser pulse. In this scheme, we use a perturbative 400-nm laser field to modulate the photoelectron holographic fringes generated by a strong 800-nm pulse. The fringe shift offers a direct experimental access to the intermediate canonical momentum of the rescattering electron, allowing us to reconstruct the momentum offset at the tunnel exit with high accuracy. Our result unambiguously proves the existence of nonzero initial longitudinal momentum at the tunnel exit and provides fundamental insights into the non-quasi-static nature of the strong-field tunneling.

physics.atom-ph↗