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Peixuan Li

Publications and source records attributed to Peixuan Li.

15 recordsLinked to original sources
arXiv2026

Realization of Air-Stable Two-Dimensional Superconductor Nb2Pd3Te5 With Quasi-One-Dimensional Pair Density Modulation

Two-dimensional (2D) superconductors provide a fertile platform for exploring reduced-dimensional superconductivity and emergent quantum phenomena. Incorporating quasi-one-dimensional (quasi-1D) structural motifs into 2D superconductors offers a powerful route to engineer strong electronic anisotropy, enabling unconventional superconducting states and anisotropic superconducting transport functionalities. However, such systems remain rarely realized. Here we report the realization of a 2D superconductor Nb2Pd3Te5, exhibiting an intrinsic quasi-1D pair density modulation. Monolayer and bilayer Nb2Pd3Te5 is synthesized via van-der-Waals epitaxy. Using ultralow-temperature scanning tunneling microscopy/spectroscopy, we observe the quasi-1D crystal structure and superconductivity below ~0.6 K with a pronounced quasi-1D pair density modulation. Remarkably, both monolayer and bilayer Nb2Pd3Te5 show strong air stability. Our findings establish atomically 2D Nb2Pd3Te5 as a robust and promising platform for exploring novel low-dimensional quantum phenomena and anisotropy-enabled superconducting devices.

cond-mat.mtrl-sci↗
arXiv2026

Compositional security definitions for higher-order where declassification

To ensure programs do not leak private data, we often want to be able to provide formal guarantees ensuring such data is handled correctly. Often, we cannot keep such data secret entirely; instead programmers specify how private data may be declassified. While security definitions for declassification exist, they mostly do not handle higher-order programs. In fact, in the higher-order setting no compositional security definition exists for intensional information-flow properties such as where declassification, which allows declassification in specific parts of a program. We use logical relations to build a model (and thus security definition) of where declassification. The key insight required for our model is that we must stop enforcing indistinguishability once a \emph{relevant declassification} has occurred. We show that the resulting security definition provides more security than the most related previous definition, which is for the lower-order setting. This paper is an extended version of the paper of the same name published at OOPSLA 2023 ([21]).

cs.PL↗
arXiv2025

NSmark: Null Space Based Black-box Watermarking Defense Framework for Language Models

Language models (LMs) have emerged as critical intellectual property (IP) assets that necessitate protection. Although various watermarking strategies have been proposed, they remain vulnerable to Linear Functionality Equivalence Attack (LFEA), which can invalidate most existing white-box watermarks without prior knowledge of the watermarking scheme or training data. This paper analyzes and extends the attack scenarios of LFEA to the commonly employed black-box settings for LMs by considering Last-Layer outputs (dubbed LL-LFEA). We discover that the null space of the output matrix remains invariant against LL-LFEA attacks. Based on this finding, we propose NSmark, a black-box watermarking scheme that is task-agnostic and capable of resisting LL-LFEA attacks. NSmark consists of three phases: (i) watermark generation using the digital signature of the owner, enhanced by spread spectrum modulation for increased robustness; (ii) watermark embedding through an output mapping extractor that preserves the LM performance while maximizing watermark capacity; (iii) watermark verification, assessed by extraction rate and null space conformity. Extensive experiments on both pre-training and downstream tasks confirm the effectiveness, scalability, reliability, fidelity, and robustness of our approach. Code is available at https://github.com/dongdongzhaoUP/NSmark.

cs.CR↗
arXiv2024

UOR: Universal Backdoor Attacks on Pre-trained Language Models

Backdoors implanted in pre-trained language models (PLMs) can be transferred to various downstream tasks, which exposes a severe security threat. However, most existing backdoor attacks against PLMs are un-targeted and task-specific. Few targeted and task-agnostic methods use manually pre-defined triggers and output representations, which prevent the attacks from being more effective and general. In this paper, we first summarize the requirements that a more threatening backdoor attack against PLMs should satisfy, and then propose a new backdoor attack method called UOR, which breaks the bottleneck of the previous approach by turning manual selection into automatic optimization. Specifically, we define poisoned supervised contrastive learning which can automatically learn the more uniform and universal output representations of triggers for various PLMs. Moreover, we use gradient search to select appropriate trigger words which can be adaptive to different PLMs and vocabularies. Experiments show that our method can achieve better attack performance on various text classification tasks compared to manual methods. Further, we tested our method on PLMs with different architectures, different usage paradigms, and more difficult tasks, which demonstrated the universality of our method.

cs.CL↗
arXiv2024

A Gradually Reinforced Sample-Average-Approximation Differentiable Homotopy Method for a System of Stochastic Equations

This paper intends to apply the sample-average-approximation (SAA) scheme to solve a system of stochastic equations (SSE), which has many applications in a variety of fields. The SAA is an effective paradigm to address risks and uncertainty in stochastic models from the perspective of Monte Carlo principle. Nonetheless, a numerical conflict arises from the sample size of SAA when one has to make a tradeoff between the accuracy of solutions and the computational cost. To alleviate this issue, we incorporate a gradually reinforced SAA scheme into a differentiable homotopy method and develop a gradually reinforced sample-average-approximation (GRSAA) differentiable homotopy method in this paper. By introducing a series of continuously differentiable functions of the homotopy parameter $t$ ranging between zero and one, we establish a differentiable homotopy system, which is able to gradually increase the sample size of SAA as $t$ descends from one to zero. The set of solutions to the homotopy system contains an everywhere smooth path, which starts from an arbitrary point and ends at a solution to the SAA with any desired accuracy. The GRSAA differentiable homotopy method serves as a bridge to link the gradually reinforced SAA scheme and a differentiable homotopy method and retains the nice property of global convergence the homotopy method possesses while greatly reducing the computational cost for attaining a desired solution to the original SSE. Several numerical experiments further confirm the effectiveness and efficiency of the proposed method.

math.NA↗
arXiv2023

FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning

Federated learning (FL) has enabled global model training on decentralized data in a privacy-preserving way by aggregating model updates. However, for many natural language processing (NLP) tasks that utilize pre-trained language models (PLMs) with large numbers of parameters, there are considerable communication costs associated with FL. Recently, prompt tuning, which tunes some soft prompts without modifying PLMs, has achieved excellent performance as a new learning paradigm. Therefore we want to combine the two methods and explore the effect of prompt tuning under FL. In this paper, we propose "FedPrompt" to study prompt tuning in a model split aggregation way using FL, and prove that split aggregation greatly reduces the communication cost, only 0.01% of the PLMs' parameters, with little decrease on accuracy both on IID and Non-IID data distribution. This improves the efficiency of FL method while also protecting the data privacy in prompt tuning. In addition, like PLMs, prompts are uploaded and downloaded between public platforms and personal users, so we try to figure out whether there is still a backdoor threat using only soft prompts in FL scenarios. We further conduct backdoor attacks by data poisoning on FedPrompt. Our experiments show that normal backdoor attack can not achieve a high attack success rate, proving the robustness of FedPrompt. We hope this work can promote the application of prompt in FL and raise the awareness of the possible security threats.

cs.LG↗
arXiv2022

Cost-effective photonic super-resolution millimeter-wave joint radar-communication system using self-coherent detection

A cost-effective millimeter-wave (MMW) joint radar-communication (JRC) system with super resolution is proposed and experimentally demonstrated, using optical heterodyne up-conversion and self-coherent detection down-conversion techniques. The point lies in the designed coherent dual-band constant envelope linear frequency modulation-orthogonal frequency division multiplexing (LFM-OFDM) signal with opposite phase modulation indexes for the JRC system. Then the self-coherent detection, as a simple and low-cost means, is accordingly facilitated for both de-chirping of MMW radar and frequency down-conversion reception of MMW communication, which circumvents the costly high-speed mixers along with MMW local oscillators and more significantly achieves the real-time decomposition of radar and communication information. Furthermore, a super resolution radar range profile is realized through the coherent fusion processing of dual-band JRC signal. In experiments, a dual-band LFM-OFDM JRC signal centered at 54-GHz and 61-GHz is generated. The dual bands are featured with an identical instantaneous bandwidth of 2 GHz and carry an OFDM signal of 1 GBaud, which help to achieve a 6-Gbit/s data rate for communication and a 1.76-cm range resolution for radar.

eess.SP↗
arXiv2022

Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving

While separately leveraging monocular 3D object detection and 2D multi-object tracking can be straightforwardly applied to sequence images in a frame-by-frame fashion, stand-alone tracker cuts off the transmission of the uncertainty from the 3D detector to tracking while cannot pass tracking error differentials back to the 3D detector. In this work, we propose jointly training 3D detection and 3D tracking from only monocular videos in an end-to-end manner. The key component is a novel spatial-temporal information flow module that aggregates geometric and appearance features to predict robust similarity scores across all objects in current and past frames. Specifically, we leverage the attention mechanism of the transformer, in which self-attention aggregates the spatial information in a specific frame, and cross-attention exploits relation and affinities of all objects in the temporal domain of sequence frames. The affinities are then supervised to estimate the trajectory and guide the flow of information between corresponding 3D objects. In addition, we propose a temporal -consistency loss that explicitly involves 3D target motion modeling into the learning, making the 3D trajectory smooth in the world coordinate system. Time3D achieves 21.4\% AMOTA, 13.6\% AMOTP on the nuScenes 3D tracking benchmark, surpassing all published competitors, and running at 38 FPS, while Time3D achieves 31.2\% mAP, 39.4\% NDS on the nuScenes 3D detection benchmark.

cs.CV↗
arXiv2021

Towards a General-Purpose Dynamic Information Flow Policy

Noninterference offers a rigorous end-to-end guarantee for secure propagation of information. However, real-world systems almost always involve security requirements that change during program execution, making noninterference inapplicable. Prior works alleviate the limitation to some extent, but even for a veteran in information flow security, understanding the subtleties in the syntax and semantics of each policy is challenging, largely due to very different policy specification languages, and more fundamentally, semantic requirements of each policy. We take a top-down approach and present a novel information flow policy, called Dynamic Release, which allows information flow restrictions to downgrade and upgrade in arbitrary ways. Dynamic Release is formalized on a novel framework that, for the first time, allows us to compare and contrast various dynamic policies in the literature. We show that Dynamic Release generalizes declassification, erasure, delegation and revocation. Moreover, it is the only dynamic policy that is both applicable and correct on a benchmark of tests with dynamic policy.

cs.CR↗
arXiv2020

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

Although the recent image-based 3D object detection methods using Pseudo-LiDAR representation have shown great capabilities, a notable gap in efficiency and accuracy still exist compared with LiDAR-based methods. Besides, over-reliance on the stand-alone depth estimator, requiring a large number of pixel-wise annotations in the training stage and more computation in the inferencing stage, limits the scaling application in the real world. In this paper, we propose an efficient and accurate 3D object detection method from stereo images, named RTS3D. Different from the 3D occupancy space in the Pseudo-LiDAR similar methods, we design a novel 4D feature-consistent embedding (FCE) space as the intermediate representation of the 3D scene without depth supervision. The FCE space encodes the object's structural and semantic information by exploring the multi-scale feature consistency warped from stereo pair. Furthermore, a semantic-guided RBF (Radial Basis Function) and a structure-aware attention module are devised to reduce the influence of FCE space noise without instance mask supervision. Experiments on the KITTI benchmark show that RTS3D is the first true real-time system (FPS$>$24) for stereo image 3D detection meanwhile achieves $10\%$ improvement in average precision comparing with the previous state-of-the-art method. The code will be available at https://github.com/Banconxuan/RTS3D

cs.CV↗
arXiv2020

Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training

In this work, we propose a novel single-shot and keypoints-based framework for monocular 3D objects detection using only RGB images, called KM3D-Net. We design a fully convolutional model to predict object keypoints, dimension, and orientation, and then combine these estimations with perspective geometry constraints to compute position attribute. Further, we reformulate the geometric constraints as a differentiable version and embed it into the network to reduce running time while maintaining the consistency of model outputs in an end-to-end fashion. Benefiting from this simple structure, we then propose an effective semi-supervised training strategy for the setting where labeled training data is scarce. In this strategy, we enforce a consensus prediction of two shared-weights KM3D-Net for the same unlabeled image under different input augmentation conditions and network regularization. In particular, we unify the coordinate-dependent augmentations as the affine transformation for the differential recovering position of objects and propose a keypoints-dropout module for the network regularization. Our model only requires RGB images without synthetic data, instance segmentation, CAD model, or depth generator. Nevertheless, extensive experiments on the popular KITTI 3D detection dataset indicate that the KM3D-Net surpasses all previous state-of-the-art methods in both efficiency and accuracy by a large margin. And also, to the best of our knowledge, this is the first time that semi-supervised learning is applied in monocular 3D objects detection. We even surpass most of the previous fully supervised methods with only 13\% labeled data on KITTI.

cs.CV↗
arXiv2020

RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving

In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without the need for external networks or supervision data. Our method is the first real-time system for monocular image 3D detection while achieves state-of-the-art performance on the KITTI benchmark. Code will be released at https://github.com/Banconxuan/RTM3D.

cs.CV↗
arXiv2017

Towards a Flow- and Path-Sensitive Information Flow Analysis: Technical Report

This paper investigates a flow- and path-sensitive static information flow analysis. Compared with security type systems with fixed labels, it has been shown that flow-sensitive type systems accept more secure programs. We show that an information flow analysis with fixed labels can be both flow- and path-sensitive. The novel analysis has two major components: 1) a general-purpose program transformation that removes false dataflow dependencies in a program that confuse a fixed-label type system, and 2) a fixed-label type system that allows security types to depend on path conditions. We formally prove that the proposed analysis enforces a rigorous security property: noninterference. Moreover, we show that the analysis is strictly more precise than a classic flow-sensitive type system, and it allows sound control of information flow in the presence of mutable variables without resorting to run-time mechanisms.

cs.PL↗
arXiv2016

Tunable Photonic Radiofrequency Filter with An Ultra-high Out-Of-Band Rejection

As radiofrequency filtering plays a vital role in electromagnetic devices and systems, recently photonic techniques have been intensively studied to implement radiofrequency filters to harness wide frequency coverage, large instantaneous bandwidth, low frequency-dependent loss, flexible tunability and strong immunity to electromagnetic interference. However, one crucial challenge facing the photonic radiofrequency filter (PRF) is the less impressive out-of-band rejection. Here, to the best of our knowledge, we demonstrate a tunable PRF with a record out-of-band rejection of 80 dB, which is 3 dB higher than the maximum value (~77 dB) reported so far, when incorporating highly selective polarization control and large narrow-band amplification enabled by stimulated Brillouin scattering effect. In particular, this record rejection is arduous to be achieved for a narrow passband (e.g., a few megahertz) and a high finesse in a PRF. Moreover, the proposed PRF is an active one capable of providing negligible insertion loss and even signal gain. Tunable central frequency ranging from 2.1 to 6.1 GHz is also demonstrated. The proposed PRF will provide an ultra-high noise or clutter suppression for harsh electromagnetic scenarios, particularly when room-temperature implementation and remote distribution are needed.

physics.optics↗