arXiv subjects

Hui Chen

Publications and source records attributed to Hui Chen.

At least 181 records · Page 10Linked to original sources

arXiv2024

AoA-Based Physical Layer Authentication in Analog Arrays under Impersonation Attacks

We discuss the use of angle of arrival (AoA) as an authentication measure in analog array multiple-input multiple-output (MIMO) systems. A base station equipped with an analog array authenticates users based on the AoA estimated from certified pilot transmissions, while active attackers manipulate their transmitted signals to mount impersonation attacks. We study several attacks of increasing intensity (captured through the availability of side information at the attackers) and assess the performance of AoA-based authentication using one-class classifiers. Our results show that some attack techniques with knowledge of the combiners at the verifier are effective in falsifying the AoA and compromising the security of the considered type of physical layer authentication.

cs.CR↗

arXiv2024

Visualization of Unconventional Rashba Band and Vortex Zero Mode in Topopogical Superconductor Candidate AuSn$_{4}$

Topological superconductivity (TSC) is a promising platform to host Majorana zero mode (MZM) for topological quantum computing. Recently, the noble metal alloy AuSn$_{4}$ has been identified as an intrinsic surface TSC. However, the atomic visualization of its nontrivial surface states and MZM remains elusive. Here, we report the direct observation of unconventional surface states and vortex zero mode at the gold (Au) terminated surfaces of AuSn$_{4}$, by ultra-low scanning tunneling microscope/spectroscopy. Distinct from the trivial metallic bulk states at tin (Sn) surfaces, the Au terminated surface exhibits pronounced surface states near Fermi level. Our density functional theory calculations indicate that these states arise from unconventional Rashba bands, where two Fermi circles from different bands share identical helical spin textures, chiralities, and group velocities in the same direction. Furthermore, we find that although the superconducting gap, critical temperature, anisotropic in-plane critical field are almost identical on Au and Sn terminated surfaces, the in-gap bound states inside Abrikosov vortex cores show significant differences. The vortex on Sn terminated surfaces exhibits a conventional Caroli-de Gennes-Matricon bound state while the Au surface shows a sharp zero-energy core state with a long non-splitting distance, resembling an MZM in a non-quantum-limit condition. This distinction may result from the dominant contribution of unconventional Rashba bands near Fermi energy from Au terminated surface. Our results provide a new platform for studying unconventional Rashba band and MZM in superconductors.

cond-mat.supr-con↗

arXiv2024

V2X Sidelink Positioning in FR1: From Ray-Tracing and Channel Estimation to Bayesian Tracking

Sidelink positioning research predominantly focuses on the snapshot positioning problem, often within the mmWave band. Only a limited number of studies have delved into vehicle-to-anything (V2X) tracking within sub-6 GHz bands. In this paper, we investigate the V2X sidelink tracking challenges over sub-6 GHz frequencies. We propose a Kalman-filter-based tracking approach that leverages the estimated error covariance lower bounds (EECLBs) as measurement covariance, alongside a gating method to augment tracking performance. Through simulations employing ray-tracing data and super-resolution channel parameter estimation, we validate the feasibility of sidelink tracking using our proposed tracking filter with two novel EECLBs. Additionally, we demonstrate the efficacy of the gating method in identifying line-of-sight paths and enhancing tracking performance.

eess.SP↗

arXiv2024

Poisson kernel and blow-up of the second derivatives near the boundary for Stokes equations with Navier boundary condition

We derive the explicit Poisson kernel of Stokes equations in the half space with nonhomogeneous Navier boundary condition (BC) for both infinite and finite slip length. By using this kernel, for any $q>1$, we construct a finite energy solution of Stokes equations with Navier BC in the half space, with bounded velocity and velocity gradient, but having unbounded second derivatives in $L^q$ locally near the boundary. While the Caccioppoli type inequality of Stokes equations with Navier BC is true for the first derivatives of velocity, which is proved by us in [CPAA 2023], this example shows that the corresponding inequality for the second derivatives of the velocity is not true. Moreover, we give an alternative proof of the blow-up using a shear flow example, which is simple and is the solution of both Stokes and Navier--Stokes equations.

math.AP↗

arXiv2024

Towards Robust Instruction Tuning on Multimodal Large Language Models

Fine-tuning large language models (LLMs) on multi-task instruction-following data has been proven to be a powerful learning paradigm for improving their zero-shot capabilities on new tasks. Recent works about high-quality instruction-following data generation and selection require amounts of human labor to conceive model-understandable instructions for the given tasks and carefully filter the LLM-generated data. In this work, we introduce an automatic instruction augmentation method named INSTRAUG in multimodal tasks. It starts from a handful of basic and straightforward meta instructions but can expand an instruction-following dataset by 30 times. Results on two popular multimodal instructionfollowing benchmarks MULTIINSTRUCT and InstructBLIP show that INSTRAUG can significantly improve the alignment of multimodal large language models (MLLMs) across 12 multimodal tasks, which is even equivalent to the benefits of scaling up training data multiple times.

cs.CL↗

arXiv2024

Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition

Disordered speech recognition profound implications for improving the quality of life for individuals afflicted with, for example, dysarthria. Dysarthric speech recognition encounters challenges including limited data, substantial dissimilarities between dysarthric and non-dysarthric speakers, and significant speaker variations stemming from the disorder. This paper introduces Perceiver-Prompt, a method for speaker adaptation that utilizes P-Tuning on the Whisper large-scale model. We first fine-tune Whisper using LoRA and then integrate a trainable Perceiver to generate fixed-length speaker prompts from variable-length inputs, to improve model recognition of Chinese dysarthric speech. Experimental results from our Chinese dysarthric speech dataset demonstrate consistent improvements in recognition performance with Perceiver-Prompt. Relative reduction up to 13.04% in CER is obtained over the fine-tuned Whisper.

eess.AS↗

arXiv2024

ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks

The recently proposed Bayesian Flow Networks~(BFNs) show great potential in modeling parameter spaces, offering a unified strategy for handling continuous, discretized, and discrete data. However, BFNs cannot learn high-level semantic representation from the parameter space since {common encoders, which encode data into one static representation, cannot capture semantic changes in parameters.} This motivates a new direction: learning semantic representations hidden in the parameter spaces to characterize mixed-typed noisy data. {Accordingly, we propose a representation learning framework named ParamReL, which operates in the parameter space to obtain parameter-wise latent semantics that exhibit progressive structures. Specifically, ParamReL proposes a \emph{self-}encoder to learn latent semantics directly from parameters, rather than from observations. The encoder is then integrated into BFNs, enabling representation learning with various formats of observations. Mutual information terms further promote the disentanglement of latent semantics and capture meaningful semantics simultaneously.} We illustrate {conditional generation and reconstruction} in ParamReL via expanding BFNs, and extensive {quantitative} experimental results demonstrate the {superior effectiveness} of ParamReL in learning parameter representation.

cs.LG↗

arXiv2024

Ghost imaging-based Non-contact Heart Rate Detection

Remote heart rate measurement is an increasingly concerned research field, usually using remote photoplethysmography (rPPG) to collect heart rate information through video data collection. However, in certain specific scenarios (such as low light conditions, intense lighting, and non-line-of-sight situations), traditional imaging methods fail to capture image information effectively, that may lead to difficulty or inability in measuring heart rate. To address these limitations, this study proposes using ghost imaging as a substitute for traditional imaging in the aforementioned scenarios. The mean absolute error between experimental measurements and reference true values is 4.24 bpm.Additionally, the bucket signals obtained by the ghost imaging system can be directly processed using digital signal processing techniques, thereby enhancing personal privacy protection.

eess.IV↗

arXiv2024

Evolution of static to dynamic mechanical behavior in topological nonreciprocal robotic metamaterials

Based on the Maxwell-Beatty reciprocity theorem, static non-reciprocity has been realized by using nonlinearity, but this non-reciprocity has strict restrictions on input amplitude and structure size (number of units). Here, we propose a robotic metamaterial with two components of displacement and rotation, which uses active control to add external forces on the units to break reciprocity at the level of the interactions between the units. We show analytically and simulatively that breaking reciprocity at the level of the interactions directly leads to a strong asymmetric response of displacement in a static system, this displacement-specific characteristic not only has no restrictions on size, input amplitude, and suitable geometric asymmetry, but also can be transmitted to rotation by coupling under large deformation. After the evolution from statics to dynamics, asymmetric transmission and unidirectional amplification of vector solitons are both implemented in this system. Our research uncovers the evolution of static non-reciprocity to dynamic non-reciprocity while building a bridge between non-reciprocity physics and soliton science.

cond-mat.soft↗

arXiv2024

Leveraging Machine Learning for Advanced Nanoscale X-ray Analysis: Unmixing Multicomponent Signals and Enhancing Chemical Quantification

Energy dispersive X-ray (EDX) spectroscopy in the transmission electron microscope is a key tool for nanomaterials analysis, providing a direct link between spatial and chemical information. However, using it for precisely determining chemical compositions presents challenges of noisy data from low X-ray yields and mixed signals from phases that overlap along the electron beam trajectory. Here, we introduce a novel method, non-negative matrix factorisation based pan-sharpening (PSNMF), to address these limitations. Leveraging the Poisson nature of EDX spectral noise and binning operations, PSNMF retrieves high quality phase spectral and spatial signatures via consecutive factorisations. After validating PSNMF with synthetic datasets of different noise levels, we illustrate its effectiveness on two distinct experimental cases: a nano-mineralogical lamella, and supported catalytic nanoparticles. Not only does PSNMF obtain accurate phase signatures, datasets reconstructed from the outputs have demonstrably lower noise and better fidelity than from the benchmark denoising method of principle component analysis.

cond-mat.mtrl-sci↗

arXiv2024

Beamforming Inferring by Conditional WGAN-GP for Holographic Antenna Arrays

The beamforming technology with large holographic antenna arrays is one of the key enablers for the next generation of wireless systems, which can significantly improve the spectral efficiency. However, the deployment of large antenna arrays implies high algorithm complexity and resource overhead at both receiver and transmitter ends. To address this issue, advanced technologies such as artificial intelligence have been developed to reduce beamforming overhead. Intuitively, if we can implement the near-optimal beamforming only using a tiny subset of the all channel information, the overhead for channel estimation and beamforming would be reduced significantly compared with the traditional beamforming methods that usually need full channel information and the inversion of large dimensional matrix. In light of this idea, we propose a novel scheme that utilizes Wasserstein generative adversarial network with gradient penalty to infer the full beamforming matrices based on very little of channel information. Simulation results confirm that it can accomplish comparable performance with the weighted minimum mean-square error algorithm, while reducing the overhead by over 50%.

cs.IT↗

arXiv2024

From STEM-EDXS data to phase separation and quantification using physics-guided NMF

We present the development of a new algorithm which combines state-of-the-art energy-dispersive X-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spectrum images. The algorithm is based on non-negative matrix factorization (NMF) incorporating a physics-guided factorization model. It optimizes a Poisson likelihood, under additional simplex constraint together with user-chosen sparsity-inducing and smoothing regularizations, and is based on iterative multiplicative updates. The fluorescence of X-rays is fully modeled thanks to state-of-the-art theoretical work. It is shown that the output of the algorithm can be used for a direct chemical quantification. With this approach, it is straightforward to include a priori knowledge on the specimen such as the presence or absence of certain chemical elements in some of its phases. This work is implemented within two open-source Python packages, espm and emtables, which are used here for data simulation, data analysis and quantification. Using simulated data, we demonstrate that incorporating physical modeling in the decomposition helps retrieve meaningful components from spatially and spectrally mixed phases, even when the data are very noisy. For synthetic data with a higher signal, the regularizations yield a tenfold increase in the quality of the reconstructed abundance maps compared to standard NMF. Our approach is further validated on experimental data with a known ground truth, where state-of-the art results are achieved by using prior knowledge about the sample. Our model can be generalized to any other scanning spectroscopy techniques where underlying physical modeling can be linearized.

cond-mat.mtrl-sci↗

arXiv2024

More is Better: Deep Domain Adaptation with Multiple Sources

In many practical applications, it is often difficult and expensive to obtain large-scale labeled data to train state-of-the-art deep neural networks. Therefore, transferring the learned knowledge from a separate, labeled source domain to an unlabeled or sparsely labeled target domain becomes an appealing alternative. However, direct transfer often results in significant performance decay due to domain shift. Domain adaptation (DA) aims to address this problem by aligning the distributions between the source and target domains. Multi-source domain adaptation (MDA) is a powerful and practical extension in which the labeled data may be collected from multiple sources with different distributions. In this survey, we first define various MDA strategies. Then we systematically summarize and compare modern MDA methods in the deep learning era from different perspectives, followed by commonly used datasets and a brief benchmark. Finally, we discuss future research directions for MDA that are worth investigating.

cs.CV↗

arXiv2024

FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification

While deep neural networks (DNNs) based personalized federated learning (PFL) is demanding for addressing data heterogeneity and shows promising performance, existing methods for federated learning (FL) suffer from efficient systematic uncertainty quantification. The Bayesian DNNs-based PFL is usually questioned of either over-simplified model structures or high computational and memory costs. In this paper, we introduce FedSI, a novel Bayesian DNNs-based subnetwork inference PFL framework. FedSI is simple and scalable by leveraging Bayesian methods to incorporate systematic uncertainties effectively. It implements a client-specific subnetwork inference mechanism, selects network parameters with large variance to be inferred through posterior distributions, and fixes the rest as deterministic ones. FedSI achieves fast and scalable inference while preserving the systematic uncertainties to the fullest extent. Extensive experiments on three different benchmark datasets demonstrate that FedSI outperforms existing Bayesian and non-Bayesian FL baselines in heterogeneous FL scenarios.

cs.LG↗

arXiv2024

Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models

Video question-answering is a fundamental task in the field of video understanding. Although current vision--language models (VLMs) equipped with Video Transformers have enabled temporal modeling and yielded superior results, they are at the cost of huge computational power and thus too expensive to deploy in real-time application scenarios. An economical workaround only samples a small portion of frames to represent the main content of that video and tune an image--text model on these sampled frames. Recent video understanding models usually randomly sample a set of frames or clips, regardless of internal correlations between their visual contents, nor their relevance to the problem. We argue that such kinds of aimless sampling may omit the key frames from which the correct answer can be deduced, and the situation gets worse when the sampling sparsity increases, which always happens as the video lengths increase. To mitigate this issue, we propose two frame sampling strategies, namely the most domain frames (MDF) and most implied frames (MIF), to maximally preserve those frames that are most likely vital to the given questions. MDF passively minimizes the risk of key frame omission in a bootstrap manner, while MIS actively searches key frames customized for each video--question pair with the assistance of auxiliary models. The experimental results on three public datasets from three advanced VLMs (CLIP, GIT and All-in-one) demonstrate that our proposed strategies can boost the performance for image-text pretrained models. The source codes pertaining to the method proposed in this paper are publicly available at https://github.com/declare-lab/sas-vqa.

cs.CV↗

arXiv2024

Integrated Communication, Localization, and Sensing in 6G D-MIMO Networks

Future generations of mobile networks call for concurrent sensing and communication functionalities in the same hardware and/or spectrum. Compared to communication, sensing services often suffer from limited coverage, due to the high path loss of the reflected signal and the increased infrastructure requirements. To provide a more uniform quality of service, distributed multiple input multiple output (D-MIMO) systems deploy a large number of distributed nodes and efficiently control them, making distributed integrated sensing and communications (ISAC) possible. In this paper, we investigate ISAC in D-MIMO through the lens of different design architectures and deployments, revealing both conflicts and synergies. In addition, simulation and demonstration results reveal both opportunities and challenges towards the implementation of ISAC in D-MIMO.

cs.IT↗

arXiv2024

RepViT: Revisiting Mobile CNN From ViT Perspective

Recently, lightweight Vision Transformers (ViTs) demonstrate superior performance and lower latency, compared with lightweight Convolutional Neural Networks (CNNs), on resource-constrained mobile devices. Researchers have discovered many structural connections between lightweight ViTs and lightweight CNNs. However, the notable architectural disparities in the block structure, macro, and micro designs between them have not been adequately examined. In this study, we revisit the efficient design of lightweight CNNs from ViT perspective and emphasize their promising prospect for mobile devices. Specifically, we incrementally enhance the mobile-friendliness of a standard lightweight CNN, \ie, MobileNetV3, by integrating the efficient architectural designs of lightweight ViTs. This ends up with a new family of pure lightweight CNNs, namely RepViT. Extensive experiments show that RepViT outperforms existing state-of-the-art lightweight ViTs and exhibits favorable latency in various vision tasks. Notably, on ImageNet, RepViT achieves over 80\% top-1 accuracy with 1.0 ms latency on an iPhone 12, which is the first time for a lightweight model, to the best of our knowledge. Besides, when RepViT meets SAM, our RepViT-SAM can achieve nearly 10$\times$ faster inference than the advanced MobileSAM. Codes and models are available at \url{https://github.com/THU-MIG/RepViT}.

cs.CV↗

arXiv2024

Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects

Personal digital data is a critical asset, and governments worldwide have enforced laws and regulations to protect data privacy. Data users have been endowed with the right to be forgotten of their data. In the course of machine learning (ML), the forgotten right requires a model provider to delete user data and its subsequent impact on ML models upon user requests. Machine unlearning emerges to address this, which has garnered ever-increasing attention from both industry and academia. While the area has developed rapidly, there is a lack of comprehensive surveys to capture the latest advancements. Recognizing this shortage, we conduct an extensive exploration to map the landscape of machine unlearning including the (fine-grained) taxonomy of unlearning algorithms under centralized and distributed settings, debate on approximate unlearning, verification and evaluation metrics, challenges and solutions for unlearning under different applications, as well as attacks targeting machine unlearning. The survey concludes by outlining potential directions for future research, hoping to serve as a guide for interested scholars.

cs.LG↗