arXiv subjects

Renjie Xie

Publications and source records attributed to Renjie Xie.

At least 19 recordsLinked to original sources

arXiv2026

HeadWiseKV: Budgeted Per-Head Cache Residency for Hybrid Long-Context Language Models

Long-context inference retains a growing key--value (KV) cache during decoding, which consumes substantial GPU memory and can reduce generation throughput. This bottleneck remains in hybrid language models because their residual global-attention layers can dominate context-dependent cache demand. We study how to allocate this state under an aggregate KV-residency budget. We introduce HeadWiseKV, a training-free framework that compresses the residual global KV caches of hybrid language models while preserving their native local, recurrent, and linear paths. It assigns each physical KV head a static, multilevel history window, making cache demand predictable before serving. We formulate this allocation as a restricted operational rate--distortion problem and propose SeqCalib as the core policy-generation algorithm in HeadWiseKV. SeqCalib processes layers in execution order and conditions each decision on the lower-layer policy used at deployment, thereby accounting for interactions across depth. A grouped-cache runtime materializes the selected policy as actual per-head KV residency rather than a mask over a full cache. We evaluate downstream quality across four hybrid long-context models and study physical residency and serving behavior on Qwen3.6-27B. HeadWiseKV retains near-Full-KV RULER and LoCoMo quality across the evaluated models. In the fixed-model systems study, it reduces sampled peak device memory by 8.59\% at a 112K context length and extends the largest verified successful context from 114K to 161K.

cs.AI↗

arXiv2026

Revisiting Black-Box Model Ownership Verification through Information Theory

Modern machine learning models require substantial computational resources and data to train, making them valuable intellectual property. Model watermarking has emerged as a practical solution for black-box ownership verification, but existing methods suffer from a persistent trade-off between robustness and predictive utility. In this work, we analyze this limitation from an information-theoretic perspective and identify a fundamental capacity crisis: relying solely on predicted labels provides insufficient capacity to embed robust ownership signals without degrading accuracy. To deal with it, we propose a new black-box ownership verification framework that leverages the top-k output. By exploiting this richer output space, our approach increases the effective capacity available for watermarking while preserving predictive performance. Extensive experiments across image, text, and tabular domains demonstrate that our method achieves a more favorable robustness--utility trade-off than existing approaches, while remaining practical for real-world deployment.

cs.CR↗

arXiv2026

Superconducting PdTe Thin Film Via Topotactic Transformation, Toward Topological Superconductors

Topological superconductors (TSCs) hosting Majorana zero modes (MZMs) offer a pathway to fault-tolerant quantum computation. PdTe is a promising TSC candidate due to its topological surface states and a reasonable superconducting critical temperature of ~4.5 K. However, it has been challenging to grow PdTe thin films with bulk-like superconducting properties. Here, we show that high-quality, superconducting PdTe thin films can be grown using molecular beam epitaxy (MBE). The films exhibit a sharp superconducting transition (T_onset = 4.43 K with transition width of 0.06 K), comparable to that of bulk crystals. This was made possible via a topotactic transformation from a PdTe_2 buffer layer to a PdTe phase by growing Pd on top under Te-deficient conditions. Structural and transport analyses confirm the NiAs-type structure of PdTe, as well as its two-dimensional superconducting behavior and excellent air stability. These findings suggest that the MBE-grown PdTe films and their heterostructures are a promising platform for topological superconductivity and Majorana physics.

cond-mat.supr-con↗

arXiv2026

Rapid LoRA Aggregation for Wireless Channel Adaptation in Open-Set Radio Frequency Fingerprinting

Radio frequency fingerprints (RFFs) enable secure wireless authentication but struggle in open-set scenarios with unknown devices and varying channels. Existing methods face challenges in generalization and incur high computational costs. We propose a lightweight, self-adaptive RFF extraction framework using Low-Rank Adaptation (LoRA). By pretraining LoRA modules per environment, our method enables fast adaptation to unseen channel conditions without full retraining. During inference, a weighted combination of LoRAs dynamically enhances feature extraction. Experimental results demonstrate a 15% reduction in equal error rate (EER) compared to non-finetuned baselines and an 83% decrease in training time relative to full fine-tuning, using the same training dataset. This approach provides a scalable and efficient solution for open-set RFF authentication in dynamic wireless vehicular networks.

eess.SP↗

arXiv2026

Interface-Enhanced Superconductivity in Ultrathin TiN Proximitized by Topological Insulators

High-quality topological insulator-superconductor (TI-SC) heterostructure with an atomically sharp and well-controlled interface is crucial for realizing topological superconductivity and topological quantum qubit. In particular, many studies of TI-SC heterostructures have focused on inducing superconducting gap in the TI layer via proximity effect, while the active manipulation of superconductivity in the SC layer remains largely unexplored. In this work, we fabricated TI/TiN heterostructures using highly air-stable, ultrathin TiN films as the SC layer, and observed an interface-enhanced superconductivity that contrasts with the conventional proximity effect in superconductor-normal metal interface. Band structure measurements reveal a consistent shift of Dirac point with Tc enhancement. Interfacial charge transfer provides a plausible explanation for this shift based on the systematic analysis and is therefore a likely contributor to the observed Tc enhancement. First principles calculations elucidate the charge transfer pathways, highlighting the critical role of the interfacial BiTe (BiSe)bilayer. Our results not only provide a tunable TI-SC hybrid system with robust superconductivity at ultrathin thickness, but also offer a potential route for manipulating superconductivity in TI-SC heterostructures via interface engineering.

cond-mat.mtrl-sci↗

arXiv2025

Tuning Carrier Type and Density in Highly Conductive and Infrared-Transparent (Bi1-xSbx)2Te3 films

Infrared transparent conductors have long been sought due to their broad optoelectronic applications in the infrared wavelength range. However, the search for ideal materials has been limited by the inherent trade-off between electrical conductance and optical transmittance. Band engineering offers an effective approach to modulate carrier type and density, enabling concurrent tuning of both conductance and transmittance. In this work, we present a band engineering strategy that enables effective tuning of both infrared transmittance and electrical conductance in topological insulator (Bi1-xSbx)2Te3, bridging the gap and paving the way for applying topological insulators to infrared photoelectric devices. More importantly, with the combination of high carrier mobility and a large optical dielectric constant as suggested by previous report, Sb2Te3 achieves a high electrical conductance (~1000 S/cm) and outstanding infrared transmittance (92.3%) in the wavelength range of 8~13 um, demonstrating strong potential as an infrared transparent conductor. Our findings reveal that concurrent enhancement of both carrier mobility and optical dielectric constant is key to overcoming the conductance-transmittance trade-off. This work provides valuable insight for the exploration of high-performance infrared transparent conducting materials.

cond-mat.mtrl-sci↗

arXiv2025

Channel-Aware Deep Learning for Superimposed Pilot Power Allocation and Receiver Design

Superimposed pilot (SIP) schemes face significant challenges in effectively superimposing and separating pilot and data signals, especially in multiuser mobility scenarios with rapidly varying channels. To address these challenges, we propose a novel channel-aware learning framework for SIP schemes, termed CaSIP, that jointly optimizes pilot-data power (PDP) allocation and a receiver network for pilot-data interference (PDI) elimination, by leveraging channel path gain information, a form of large-scale channel state information (CSI). The proposed framework identifies user-specific, resource element-wise PDP factors and develops a deep neural network-based SIP receiver comprising explicit channel estimation and data detection components. To properly leverage path gain data, we devise an embedding generator that projects it into embeddings, which are then fused with intermediate feature maps of the channel estimation network. Simulation results demonstrate that CaSIP efficiently outperforms traditional pilot schemes and state-of-the-art SIP schemes in terms of sum throughput and channel estimation accuracy, particularly under high-mobility and low signal-to-noise ratio (SNR) conditions.

eess.SP↗

arXiv2024

Research on Dangerous Flight Weather Prediction based on Machine Learning

With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather and ensure the safe flight of aircraft is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather, especially for meteorological conditions with high uncertainty such as storms and turbulence. SVM is a supervised learning method that distinguishes between different classes of data by finding optimal decision boundaries in a high-dimensional space. In order to meet the needs of this study, we chose the radial basis function (RBF) as the kernel function, which helps to deal with nonlinear problems and enables the model to better capture complex meteorological data structures. During the model training phase, we used historical meteorological observations from multiple weather stations, including temperature, humidity, wind speed, wind direction, and other meteorological indicators closely related to flight safety. From this data, the SVM model learns how to distinguish between normal and dangerous flight weather conditions.

cs.AI↗

arXiv2024

Resilient Growth of Highly Crystalline Topological Insulator-Superconductor Heterostructure Enabled by Ex-situ Nitride Film

Highly crystalline and easily feasible topological insulator-superconductor (TI-SC) heterostructures are crucial for the development of practical topological qubit devices. The optimal superconducting layer for TI-SC heterostructures should be highly resilient against external contaminations and structurally compatible with TIs. In this study, we provide a solution to this challenge by showcasing the growth of a highly crystalline TI-SC heterostructure using refractory TiN (111) as the superconducting layer. This approach can eliminate the need for in-situ cleaving or growth. More importantly, the TiN surface shows high resilience against contaminations during air exposure, as demonstrated by the successful recyclable growth of Bi2Se3. Our findings indicate that TI-SC heterostructures based on nitride films are compatible with device fabrication techniques, paving a path to the realization of practical topological qubit devices in the future.

cond-mat.mes-hall↗

arXiv2024

Deep CSI Compression for Dual-Polarized Massive MIMO Channels with Disentangled Representation Learning

Channel state information (CSI) feedback is critical for achieving the promised advantages of enhancing spectral and energy efficiencies in massive multiple-input multiple-output (MIMO) wireless communication systems. Deep learning (DL)-based methods have been proven effective in reducing the required signaling overhead for CSI feedback. In practical dual-polarized MIMO scenarios, channels in the vertical and horizontal polarization directions tend to exhibit high polarization correlation. To fully exploit the inherent propagation similarity within dual-polarized channels, we propose a disentangled representation neural network (NN) for CSI feedback, referred to as DiReNet. The proposed DiReNet disentangles dual-polarized CSI into three components: polarization-shared information, vertical polarization-specific information, and horizontal polarization-specific information. This disentanglement of dual-polarized CSI enables the minimization of information redundancy caused by the polarization correlation and improves the performance of CSI compression and recovery. Additionally, flexible quantization and network extension schemes are designed. Consequently, our method provides a pragmatic solution for CSI feedback to harness the physical MIMO polarization as a priori information. Our experimental results show that the performance of our proposed DiReNet surpasses that of existing DL-based networks, while also effectively reducing the number of network parameters by nearly one third.

cs.IT↗

arXiv2024

From Hardware Fingerprint to Access Token: Enhancing the Authentication on IoT Devices

The proliferation of consumer IoT products in our daily lives has raised the need for secure device authentication and access control. Unfortunately, these resource-constrained devices typically use token-based authentication, which is vulnerable to token compromise attacks that allow attackers to impersonate the devices and perform malicious operations by stealing the access token. Using hardware fingerprints to secure their authentication is a promising way to mitigate these threats. However, once attackers have stolen some hardware fingerprints (e.g., via MitM attacks), they can bypass the hardware authentication by training a machine learning model to mimic fingerprints or reusing these fingerprints to craft forge requests. In this paper, we present MCU-Token, a secure hardware fingerprinting framework for MCU-based IoT devices even if the cryptographic mechanisms (e.g., private keys) are compromised. MCU-Token can be easily integrated with various IoT devices by simply adding a short hardware fingerprint-based token to the existing payload. To prevent the reuse of this token, we propose a message mapping approach that binds the token to a specific request via generating the hardware fingerprints based on the request payload. To defeat the machine learning attacks, we mix the valid fingerprints with poisoning data so that attackers cannot train a usable model with the leaked tokens. MCU-Token can defend against armored adversary who may replay, craft, and offload the requests via MitM or use both hardware (e.g., use identical devices) and software (e.g., machine learning attacks) strategies to mimic the fingerprints. The system evaluation shows that MCU-Token can achieve high accuracy (over 97%) with a low overhead across various IoT devices and application scenarios.

cs.CR↗

arXiv2023

VeriDIP: Verifying Ownership of Deep Neural Networks through Privacy Leakage Fingerprints

Deploying Machine Learning as a Service gives rise to model plagiarism, leading to copyright infringement. Ownership testing techniques are designed to identify model fingerprints for verifying plagiarism. However, previous works often rely on overfitting or robustness features as fingerprints, lacking theoretical guarantees and exhibiting under-performance on generalized models. In this paper, we propose a novel ownership testing method called VeriDIP, which verifies a DNN model's intellectual property. VeriDIP makes two major contributions. (1) It utilizes membership inference attacks to estimate the lower bound of privacy leakage, which reflects the fingerprint of a given model. The privacy leakage fingerprints highlight the unique patterns through which the models memorize sensitive training datasets. (2) We introduce a novel approach using less private samples to enhance the performance of ownership testing. Extensive experimental results confirm that VeriDIP is effective and efficient in validating the ownership of deep learning models trained on both image and tabular datasets. VeriDIP achieves comparable performance to state-of-the-art methods on image datasets while significantly reducing computation and communication costs. Enhanced VeriDIP demonstrates superior verification performance on generalized deep learning models, particularly on table-trained models. Additionally, VeriDIP exhibits similar effectiveness on utility-preserving differentially private models compared to non-differentially private baselines.

cs.CR↗

arXiv2023

Self-information Domain-based Neural CSI Compression with Feature Coupling

Deep learning (DL)-based channel state information (CSI) feedback methods compressed the CSI matrix by exploiting its delay and angle features straightforwardly, while the measure in terms of information contained in the CSI matrix has rarely been considered. Based on this observation, we introduce self-information as an informative CSI representation from the perspective of information theory, which reflects the amount of information of the original CSI matrix in an explicit way. Then, a novel DL-based network is proposed for temporal CSI compression in the self-information domain, namely SD-CsiNet. The proposed SD-CsiNet projects the raw CSI onto a self-information matrix in the newly-defined self-information domain, extracts both temporal and spatial features of the self-information matrix, and then couples these two features for effective compression. Experimental results verify the effectiveness of the proposed SD-CsiNet by exploiting the self-information of CSI. Particularly for compression ratios 1/8 and 1/16, the SD-CsiNet respectively achieves 7.17 dB and 3.68 dB performance gains compared to state-of-the-art methods.

cs.IT↗

arXiv2023

Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective

Recently, graph-based models designed for downstream tasks have significantly advanced research on graph neural networks (GNNs). GNN baselines based on neural message-passing mechanisms such as GCN and GAT perform worse as the network deepens. Therefore, numerous GNN variants have been proposed to tackle this performance degradation problem, including many deep GNNs. However, a unified framework is still lacking to connect these existing models and interpret their effectiveness at a high level. In this work, we focus on deep GNNs and propose a novel view for understanding them. We establish a theoretical framework via inference on a probabilistic graphical model. Given the fixed point equation (FPE) derived from the variational inference on the Markov random fields, the deep GNNs, including JKNet, GCNII, DGCN, and the classical GNNs, such as GCN, GAT, and APPNP, can be regarded as different approximations of the FPE. Moreover, given this framework, more accurate approximations of FPE are brought, guiding us to design a more powerful GNN: coupling graph neural network (CoGNet). Extensive experiments are carried out on citation networks and natural language processing downstream tasks. The results demonstrate that the CoGNet outperforms the SOTA models.

cs.LG↗

arXiv2022

Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics

Deep learning (DL) applied to a device's radio-frequency fingerprint~(RFF) has attracted significant attention in physical-layer authentication due to its extraordinary classification performance. Conventional DL-RFF techniques are trained by adopting maximum likelihood estimation~(MLE). Although their discriminability has recently been extended to unknown devices in open-set scenarios, they still tend to overfit the channel statistics embedded in the training dataset. This restricts their practical applications as it is challenging to collect sufficient training data capturing the characteristics of all possible wireless channel environments. To address this challenge, we propose a DL framework of disentangled representation~(DR) learning that first learns to factor the signals into a device-relevant component and a device-irrelevant component via adversarial learning. Then, it shuffles these two parts within a dataset for implicit data augmentation, which imposes a strong regularization on RFF extractor learning to avoid the possible overfitting of device-irrelevant channel statistics, without collecting additional data from unknown channels. Experiments validate that the proposed approach, referred to as DR-based RFF, outperforms conventional methods in terms of generalizability to unknown devices even under unknown complicated propagation environments, e.g., dispersive multipath fading channels, even though all the training data are collected in a simple environment with dominated direct line-of-sight~(LoS) propagation paths.

eess.SP↗

arXiv2022

Deep CSI Compression for Massive MIMO: A Self-information Model-driven Neural Network

In order to fully exploit the advantages of massive multiple-input multiple-output (mMIMO), it is critical for the transmitter to accurately acquire the channel state information (CSI). Deep learning (DL)-based methods have been proposed for CSI compression and feedback to the transmitter. Although most existing DL-based methods consider the CSI matrix as an image, structural features of the CSI image are rarely exploited in neural network design. As such, we propose a model of self-information that dynamically measures the amount of information contained in each patch of a CSI image from the perspective of structural features. Then, by applying the self-information model, we propose a model-and-data-driven network for CSI compression and feedback, namely IdasNet. The IdasNet includes the design of a module of self-information deletion and selection (IDAS), an encoder of informative feature compression (IFC), and a decoder of informative feature recovery (IFR). In particular, the model-driven module of IDAS pre-compresses the CSI image by removing informative redundancy in terms of the self-information. The encoder of IFC then conducts feature compression to the pre-compressed CSI image and generates a feature codeword which contains two components, i.e., codeword values and position indices of the codeword values. Subsequently, the IFR decoder decouples the codeword values as well as position indices to recover the CSI image. Experimental results verify that the proposed IdasNet noticeably outperforms existing DL-based networks under various compression ratios while it has the number of network parameters reduced by orders-of-magnitude compared with various existing methods.

cs.IT↗

arXiv2021

A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication

Radio-frequency fingerprints~(RFFs) are promising solutions for realizing low-cost physical layer authentication. Machine learning-based methods have been proposed for RFF extraction and discrimination. However, most existing methods are designed for the closed-set scenario where the set of devices is remains unchanged. These methods can not be generalized to the RFF discrimination of unknown devices. To enable the discrimination of RFF from both known and unknown devices, we propose a new end-to-end deep learning framework for extracting RFFs from raw received signals. The proposed framework comprises a novel preprocessing module, called neural synchronization~(NS), which incorporates the data-driven learning with signal processing priors as an inductive bias from communication-model based processing. Compared to traditional carrier synchronization techniques, which are static, this module estimates offsets by two learnable deep neural networks jointly trained by the RFF extractor. Additionally, a hypersphere representation is proposed to further improve the discrimination of RFF. Theoretical analysis shows that such a data-and-model framework can better optimize the mutual information between device identity and the RFF, which naturally leads to better performance. Experimental results verify that the proposed RFF significantly outperforms purely data-driven DNN-design and existing handcrafted RFF methods in terms of both discrimination and network generalizability.

cs.LG↗

arXiv2021

TableGAN-MCA: Evaluating Membership Collisions of GAN-Synthesized Tabular Data Releasing

Generative Adversarial Networks (GAN)-synthesized table publishing lets people privately learn insights without access to the private table. However, existing studies on Membership Inference (MI) Attacks show promising results on disclosing membership of training datasets of GAN-synthesized tables. Different from those works focusing on discovering membership of a given data point, in this paper, we propose a novel Membership Collision Attack against GANs (TableGAN-MCA), which allows an adversary given only synthetic entries randomly sampled from a black-box generator to recover partial GAN training data. Namely, a GAN-synthesized table immune to state-of-the-art MI attacks is vulnerable to the TableGAN-MCA. The success of TableGAN-MCA is boosted by an observation that GAN-synthesized tables potentially collide with the training data of the generator. Our experimental evaluations on TableGAN-MCA have five main findings. First, TableGAN-MCA has a satisfying training data recovery rate on three commonly used real-world datasets against four generative models. Second, factors, including the size of GAN training data, GAN training epochs and the number of synthetic samples available to the adversary, are positively correlated to the success of TableGAN-MCA. Third, highly frequent data points have high risks of being recovered by TableGAN-MCA. Fourth, some unique data are exposed to unexpected high recovery risks in TableGAN-MCA, which may attribute to GAN's generalization. Fifth, as expected, differential privacy, without the consideration of the correlations between features, does not show commendable mitigation effect against the TableGAN-MCA. Finally, we propose two mitigation methods and show promising privacy and utility trade-offs when protecting against TableGAN-MCA.

cs.CR↗