arXiv subjects

Chen Gu

Publications and source records attributed to Chen Gu.

11 recordsLinked to original sources

arXiv2026

High-Performance Scaled P-Type SnOx Transistor by Atomic Layer Deposition with CFET Integration

The development of high-performance p-type oxide semiconductors is essential for realizing complementary logic for monolithic 3D integration, yet p-type oxide semiconductors still exhibit substantially inferior performance compared with their n-type counterparts. In this work, we demonstrate high-performance p-type SnOx transistors by atomic layer deposition (ALD), as back-end-of-line compatible devices for monolithic 3D integration. The SnOx transistors exhibit high field-effect mobility of 6.9 cm2/Vs, low subthreshold swing (SS) of 185 mV/dec, decent on/off ratio (ION/IOFF) of 1.8*104 and high bias stability. By scaling the channel length down to 80 nm, a high on-current of 38.7 mA/mm at VDS of -1 V is achieved. It is understood that precursor and reaction engineering to suppress Sn4+ component in SnOx film are the key for performance enhancement. Furthermore, a complementary field-effect transistor with ALD SnOx p-FET vertically stacking on ALD In2O3 n-FET is also demonstrated, achieving maximum voltage gain of 21 V/V at VDD of 4 V. These findings suggest ALD SnOx as a promising candidate for scaled high-performance BEOL p-type transistors.

cond-mat.mtrl-sci↗

arXiv2026

PromptGraph: Graph-Guided Prompt Sanitization for Balancing Privacy and Utility in LLM Inference

Large Language Model (LLM) services introduce a fundamental privacy challenge. Sensitive information may be inferred not only from explicit identifiers, such as names or phone numbers, but also from contextual associations among otherwise innocuous spans. Existing sanitizers typically assign privacy or utility signals to individual spans without explicitly modeling pairwise relationships among them. In this paper, we propose PromptGraph, a graph-guided prompt-sanitization approach for privacy-preserving LLM inference. PromptGraph estimates privacy leakage at the span level and utility-relevant contextual dependencies between pairs of spans. It represents each prompt as an attributed graph, in which nodes carry span-level privacy scores and edges encode contextual dependencies needed to preserve utility. The sanitization objective selects a protected span set that maximizes privacy gain while penalizing the loss of contextual dependencies. This formulation explicitly balances privacy and utility when contextual evidence is hidden. Protected spans are sanitized locally, and returned placeholders are restored only after passing local consistency checks. We conduct extensive experiments showing that PromptGraph achieves a more favorable balance between privacy and utility than prompt-privacy baselines.

cs.CR↗

arXiv2025

FLClear: Visually Verifiable Multi-Client Watermarking for Federated Learning

Federated learning (FL) enables multiple clients to collaboratively train a shared global model while preserving the privacy of their local data. Within this paradigm, the intellectual property rights (IPR) of client models are critical assets that must be protected. In practice, the central server responsible for maintaining the global model may maliciously manipulate the global model to erase client contributions or falsely claim sole ownership, thereby infringing on clients' IPR. Watermarking has emerged as a promising technique for asserting model ownership and protecting intellectual property. However, existing FL watermarking approaches remain limited, suffering from potential watermark collisions among clients, insufficient watermark security, and non-intuitive verification mechanisms. In this paper, we propose FLClear, a novel framework that simultaneously achieves collision-free watermark aggregation, enhanced watermark security, and visually interpretable ownership verification. Specifically, FLClear introduces a transposed model jointly optimized with contrastive learning to integrate the watermarking and main task objectives. During verification, the watermark is reconstructed from the transposed model and evaluated through both visual inspection and structural similarity metrics, enabling intuitive and quantitative ownership verification. Comprehensive experiments conducted over various datasets, aggregation schemes, and attack scenarios demonstrate the effectiveness of FLClear and confirm that it consistently outperforms state-of-the-art FL watermarking methods.

cs.LG↗

arXiv2025

High harmonic generation light source with polarization selectivity and sub-100-$\mu$m beam size for time- and angle-resolved photoemission spectroscopy

High-quality ultrafast light sources are critical for developing advanced time- and angle-resolved photoemission spectroscopy (TrARPES). While the application of high harmonic generation (HHG) light sources in TrARPES has increased significantly over the past decade, the optimization of the HHG probe beam size and selective control of the light polarization, which are important for TrARPES measurements, have been rarely explored. In this work, we report the implementation of high-quality HHG probe source with an optimum beam size down to 57 $\mu$m $\times$ 90 $\mu$m and selective light polarization control, together with mid-infrared (MIR) pumping source for TrARPES measurements using a 10 kHz amplifier laser. The selective polarization control of the HHG probe source allows to enhance bands with different orbital contributions or symmetries, as demonstrated by experimental data measured on a few representative transition metal dichalcogenide materials (TMDCs) as well as topological insulator Bi$_2$Se$_3$. Furthermore, by combining the HHG probe source with MIR pumping at 2 $\mu$m wavelength, TrARPES on a bilayer graphene shows a time resolution of 140 fs, allowing to distinguish two different relaxation processes in graphene. Such high-quality HHG probe source together with the MIR pumping expands the capability of TrARPES in revealing the ultrafast dynamics and light-induced emerging phenomena in quantum materials.

cond-mat.mes-hall↗

arXiv2025

Approximate Gaussian Mapping for Generative Image Steganography

Ordinary differential equation (ODE)-based diffusion models enable deterministic image synthesis, establishing a reversible mapping suitable for generative steganography. While prevailing methods strictly adhere to a standard normal prior, empirical evidence indicates that controlled deviations from this distribution reduce numerical inversion errors without compromising perceptual quality. Leveraging this observation, the Approximate Gaussian Mapping (AGM) is proposed as a linear transformation strategy that embeds secrets by modulating noise scale and variance. To balance retrieval numerical consistence and security, a two-stage decoupled optimization strategy is introduced to minimize the Kullback-Leibler divergence subject to target bit accuracy constraints. Beyond the proposed method, we conduct a mechanistic analysis of the divergent behaviors between pixel-space and latent-space architectures. The experimental results reveal that the VAE encoder enhances robustness by filtering external perturbations, whereas the structural regularization of the VAE decoder and the semantic variance introduced by text prompts jointly mask embedding artifacts to improve security. Experimental results confirm that pixel-space mplementations maximize embedding capacity for lossless channels, while latent-space approaches offer superior robustness and security suitable for adversarial environments

cs.CR↗

arXiv2025

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark

The development of large language models (LLMs) has raised concerns about potential misuse. One practical solution is to embed a watermark in the text, allowing ownership verification through watermark extraction. Existing methods primarily focus on defending against modification attacks, often neglecting other spoofing attacks. For example, attackers can alter the watermarked text to produce harmful content without compromising the presence of the watermark, which could lead to false attribution of this malicious content to the LLM. This situation poses a serious threat to the LLMs service providers and highlights the significance of achieving modification detection and generated-text detection simultaneously. Therefore, we propose a technique to detect modifications in text for unbiased watermark which is sensitive to modification. We introduce a new metric called ``discarded tokens", which measures the number of tokens not included in watermark detection. When a modification occurs, this metric changes and can serve as evidence of the modification. Additionally, we improve the watermark detection process and introduce a novel method for unbiased watermark. Our experiments demonstrate that we can achieve effective dual detection capabilities: modification detection and generated-text detection by watermark.

cs.CR↗

arXiv2024

Greedy selection of optimal location of sensors for uncertainty reduction in seismic moment tensor inversion

We address an optimal sensor placement problem through Bayesian experimental design for seismic full waveform inversion for the recovery of the associated moment tensor. The objective is that of optimally choosing the location of the sensors (stations) from which to collect the observed data. The Shannon expected information gain is used as the objective function to search for the optimal network of sensors. A closed form for such objective is available due to the linear structure of the forward problem, as well as the Gaussian modeling of the observational errors and prior distribution. The resulting problem being inherently combinatorial, a greedy algorithm is deployed to sequentially select the sensor locations that form the best network for learning the moment tensor. Numerical results are presented and analyzed under several instances of the problem, including: use of full three-dimensional velocity-models, cases in which the earthquake-source location is unknown, as well as moment tensor inversion under model misspecification

stat.CO↗

arXiv2022

Bayesian Neural Networks for Geothermal Resource Assessment: Prediction with Uncertainty

We consider the application of machine learning to the evaluation of geothermal resource potential. A supervised learning problem is defined where maps of 10 geological and geophysical features within the state of Nevada, USA are used to define geothermal potential across a broad region. We have available a relatively small set of positive training sites (known resources or active power plants) and negative training sites (known drill sites with unsuitable geothermal conditions) and use these to constrain and optimize artificial neural networks for this classification task. The main objective is to predict the geothermal resource potential at unknown sites within a large geographic area where the defining features are known. These predictions could be used to target promising areas for further detailed investigations. We describe the evolution of our work from defining a specific neural network architecture to training and optimization trials. Upon analysis we expose the inevitable problems of model variability and resulting prediction uncertainty. Finally, to address these problems we apply the concept of Bayesian neural networks, a heuristic approach to regularization in network training, and make use of the practical interpretation of the formal uncertainty measures they provide.

physics.geo-ph↗

arXiv2021

The Effect of Ground Truth Accuracy on the Evaluation of Localization Systems

The ability to accurately evaluate the performance of location determination systems is crucial for many applications. Typically, the performance of such systems is obtained by comparing ground truth locations with estimated locations. However, these ground truth locations are usually obtained by clicking on a map or using other worldwide available technologies like GPS. This introduces ground truth errors that are due to the marking process, map distortions, or inherent GPS inaccuracy. In this paper, we present a theoretical framework for analyzing the effect of ground truth errors on the evaluation of localization systems. Based on that, we design two algorithms for computing the real algorithmic error from the validation error and marking/map ground truth errors, respectively. We further establish bounds on different performance metrics. Validation of our theoretical assumptions and analysis using real data collected in a typical environment shows the ability of our theoretical framework to correct the estimated error of a localization algorithm in the presence of ground truth errors. Specifically, our marking error algorithm matches the real error CDF within 4%, and our map error algorithm provides a more accurate estimate of the median/tail error by 150%/72% when the map is shifted by 6m.

eess.SP↗

arXiv2019

Bayesian waveform-based calibration of high-pressure acoustic emission systems with ball drop measurements

Acoustic emission (AE) is a widely used technology to study source mechanisms and material properties during high-pressure rock failure experiments. It is important to understand the physical quantities that acoustic emission sensors measure, as well as the response of these sensors as a function of frequency. This study calibrates the newly built AE system in the MIT Rock Physics Laboratory using a ball-bouncing system. Full waveforms of multi-bounce events due to ball drops are used to infer the transfer function of lead zirconate titanate (PZT) sensors in high pressure environments. Uncertainty in the sensor transfer functions is quantified using a waveform-based Bayesian approach. The quantification of \textit{in situ} sensor transfer functions makes it possible to apply full waveform analysis for acoustic emissions at high pressures.

stat.AP↗

arXiv2016

Critical Current Survival in YBCO Superconducting Layer of the Delaminated Coated Conductor

High temperature superconducting coated conductor (CC) could be practically applied in electric equipment due to its favorable mechanical properties and the critical current performance of YBCO superconducting layer. It is well known that CC could be easily delaminated because of its poor stress tolerance in thickness direction, i.e. along the c-axis of YBCO. Commonly, a stack including YBCO layer and silver stabilizer could be obtained after the delamination. It would be interesting to investigate the superconducting properties of the delaminated stack, since it could also be considered as a new type of CC with the silver stabilizer as the buffer layer, which is quite different from the oxide buffer layers in the traditional CC and might lead to new applications. In this study, a CC sample was delaminated by liquid nitrogen immersing. A Hall probe scanning system was employed to measure the critical current (IC) distribution of the original sample and the obtained stack. It was found that IC could be partially preserved after the delamination. Dense and crack-free morphologies of the delaminated surfaces were observed by scanning electron microscopy, and the potential application of the obtained stack in superconducting joint technology was discussed.

cond-mat.supr-con↗