Yueqi Ma

Yueqi Ma, Minjie Chen, Xiang Li

While generating better negative samples for contrastive learning has been widely studied in the areas of CV and NLP, very few work has focused on graph-structured data. Recently, Mixup has been introduced to synthesize hard negative samples in graph contrastive learning (GCL). However, due to the unsupervised learning nature of GCL, without the help of soft labels, directly mixing representations of samples could inadvertently lead to the information loss of the original hard negative and further adversely affect the quality of the newly generated harder negative. To address the problem, in this paper, we propose a novel method DropMix to synthesize harder negative samples, which consists of two main steps. Specifically, we first select some hard negative samples by measuring their hardness from both local and global views in the graph simultaneously. After that, we mix hard negatives only on partial representation dimensions to generate harder ones and decrease the information loss caused by Mixup. We conduct extensive experiments to verify the effectiveness of DropMix on six benchmark datasets. Our results show that our method can lead to better GCL performance. Our data and codes are publicly available at https://github.com/Mayueq/DropMix-Code.

Yueqi Ma, Vivek Mohan, Chip-Hong Chang et al.

The Internet of Medical Things (IoMT) marks a shift toward decentralized healthcare, enabling continuous monitoring and personalized care through connected wearable and implantable devices. However, ensuring the trust and integrity of these devices themselves remains a major challenge, as physical compromise or counterfeiting can directly endanger patient safety, privacy, and data integrity. This work presents QT-PUF, a gate-tunneling-leakage-based physical unclonable function (PUF) that leverages quantum-mechanical gate leakage resulting from process-induced variations in standard CMOS devices. A differential readout circuit with a pseudo-resistor I-to-V frontend is proposed to convert the picoampere-level leakage variations into digital responses. Unlike existing PUFs such as those based on memory, ring oscillators, or arbiters, which are less suitable for ultralow-power IoMT devices (due to additional circuitry, power overhead, or poor stability), QT-PUF requires no external excitation or stabilization and operates under static bias. Simulation-based measurements for a $\mathbf{65}$~nm CMOS process demonstrate an entropy of $\mathbf{0.9999998}$, an FHD of $\mathbf{0.5001}$, and an average power (energy) consumption of $\mathbf{96.04}$~nW/bit ($\mathbf{19.21}$~fJ/bit, respectively) at $\mathbf{1.2\,V}$ and $\mathbf{35\,^{\circ}C}$ for the proposed PUF. It operates reliably across $\mathbf{0.9}\text{--}\mathbf{1.3}$~V and $\mathbf{0}\text{--}\mathbf{100\,^{\circ}C}$ with an average BER below $\mathbf{0.000163}$ across $\mathbf{1.0}\text{--}\mathbf{1.3}$~V and $\mathbf{10}\text{--}\mathbf{70\,^{\circ}C}$ within the operating conditions of typical implantable devices.