Searcharxiv⌕ Search

arXiv subjects

Ping Liu

Publications and source records attributed to Ping Liu.

At least 73 records · Page 4Linked to original sources
arXiv2025

CLIP-SR: Collaborative Linguistic and Image Processing for Super-Resolution

Convolutional Neural Networks (CNNs) have significantly advanced Image Super-Resolution (SR), yet most CNN-based methods rely solely on pixel-based transformations, often leading to artifacts and blurring, particularly under severe downsampling rates (\eg, 8$\times$ or 16$\times$). The recently developed text-guided SR approaches leverage textual descriptions to enhance their detail restoration capabilities but frequently struggle with effectively performing alignment, resulting in semantic inconsistencies. To address these challenges, we propose a multi-modal semantic enhancement framework that integrates textual semantics with visual features, effectively mitigating semantic mismatches and detail losses in highly degraded low-resolution (LR) images. Our method enables realistic, high-quality SR to be performed at large upscaling factors, with a maximum scaling ratio of 16$\times$. The framework integrates both text and image inputs using the prompt predictor, the Text-Image Fusion Block (TIFBlock), and the Iterative Refinement Module, leveraging Contrastive Language-Image Pretraining (CLIP) features to guide a progressive enhancement process with fine-grained alignment. This synergy produces high-resolution outputs with sharp textures and strong semantic coherence, even at substantial scaling factors. Extensive comparative experiments and ablation studies validate the effectiveness of our approach. Furthermore, by leveraging textual semantics, our method offers a degree of super-resolution editability, allowing for controlled enhancements while preserving semantic consistency. The code is available at https://github.com/hengliusky/CLIP-SR.

cs.CV↗
arXiv2025

Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges

As synthetic media, including video, audio, and text, become increasingly indistinguishable from real content, the risks of misinformation, identity fraud, and social manipulation escalate. This survey traces the evolution of deepfake detection from early single-modal methods to sophisticated multi-modal approaches that integrate audio-visual and text-visual cues. We present a structured taxonomy of detection techniques and analyze the transition from GAN-based to diffusion model-driven deepfakes, which introduce new challenges due to their heightened realism and robustness against detection. Unlike prior surveys that primarily focus on single-modal detection or earlier deepfake techniques, this work provides the most comprehensive study to date, encompassing the latest advancements in multi-modal deepfake detection, generalization challenges, proactive defense mechanisms, and emerging datasets specifically designed to support new interpretability and reasoning tasks. We further explore the role of Vision-Language Models (VLMs) and Multimodal Large Language Models (MLLMs) in strengthening detection robustness against increasingly sophisticated deepfake attacks. By systematically categorizing existing methods and identifying emerging research directions, this survey serves as a foundation for future advancements in combating AI-generated facial forgeries. A curated list of all related papers can be found at \href{https://github.com/qiqitao77/Comprehensive-Advances-in-Deepfake-Detection-Spanning-Diverse-Modalities}{https://github.com/qiqitao77/Awesome-Comprehensive-Deepfake-Detection}.

cs.CV↗
arXiv2025

Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator

Dataset distillation has emerged as a technique aiming to condense informative features from large, natural datasets into a compact and synthetic form. While recent advancements have refined this technique, its performance is bottlenecked by the prevailing class-specific synthesis paradigm. Under this paradigm, synthetic data is optimized exclusively for a pre-assigned one-hot label, creating an implicit class barrier in feature condensation. This leads to inefficient utilization of the distillation budget and oversight of inter-class feature distributions, which ultimately limits the effectiveness and efficiency, as demonstrated in our analysis. To overcome these constraints, this paper presents the Inter-class Feature Compensator (INFER), an innovative distillation approach that transcends the class-specific data-label framework widely utilized in current dataset distillation methods. Specifically, INFER leverages a Universal Feature Compensator (UFC) to enhance feature integration across classes, enabling the generation of multiple additional synthetic instances from a single UFC input. This significantly improves the efficiency of the distillation budget. Moreover, INFER enriches inter-class interactions during the distillation, thereby enhancing the effectiveness and generalizability of the distilled data. By allowing for the linear interpolation of labels similar to those in the original dataset, INFER meticulously optimizes the synthetic data and dramatically reduces the size of soft labels in the synthetic dataset to almost zero, establishing a new benchmark for efficiency and effectiveness in dataset distillation. In practice, INFER demonstrates state-of-the-art performance across benchmark datasets. For instance, in the ipc = 50 setting on ImageNet-1k with the same compression level, it outperforms SRe2L by 34.5% using ResNet18.

cs.CV↗
arXiv2025

Electric field control of nonlinear Hall effect in Weyl semimetal TaIrTe4

The nonlinear Hall effect (NLHE), as an important probe to reveal the symmetry breaking in topological properties of materials, opens up a new dimension for exploring the energy band structure and electron transport mechanism of quantum materials. Current studies mainly focus on the observation of material intrinsic the NLHE or inducing the NLHE response by artificially constructing corrugated/twisted twodimensionalmaterial systems. Notably, the modulation of NLHE signal strength, a core parameter of device performance, has attracted much attention, while theoretical predictions suggest that an applied electric field can achieve the NLHE enhancement through modulation of the Berry curvature dipole (BCD). Here we report effective modulation the magnitude and sign of the NLHE by applying additional constant electric fields of different directions and magnitudes in the semimetal TaIrTe4. The NLHE response strength is enhanced by 168 times compared to the intrinsic one at 4 K when the additional constant electric field of -0.5 kV/cm is applied to the b-axis of TaIrTe4 and the through a.c. current is parallel to the TaIrTe4 a-axis. Scaling law analysis suggests that the enhancement may be the result of the combined effect of the electric field on the intrinsic BCD and disorder scattering effect of TaIrTe4. This work provides a means to study the properties of TaIrTe4, as well as a valuable reference for the study of novel electronic devices.

cond-mat.mtrl-sci↗
arXiv2025

Field-free perpendicular magnetization switching of low critical current density at room temperature in TaIrTe4/ferromagnet heterostructures

Spin-orbit torque-induced perpendicular magnetization switching has attracted much attention due to the advantages of nonvolatility, high density, infinite read/write counts, and low power consumption in spintronic applications. To achieve field-free deterministic switching of perpendicular magnetization, additional magnetic field, magnetic layer assistance, or artificially designed structural symmetry breaking are usually required, which are not conducive to the high-density integration and application of low-power devices. However, 2D Weyl semimetals with low-symmetry structures have recently been found to generate z-spin-polarized currents, which may induce out-of-plane damping-like torques to their neighboring ferromagnetic layers, and realize deterministic perpendicular magnetization switching at zero magnetic field. In this Letter, we report that current-induced field-free magnetization switching at room temperature can be achieved in a perpendicularly magnetized TaIrTe4/Pt/Co/Pt device, and the critical switching current density can be lowered to be about 2.64*105 Acm-2. This study suggests that TaIrTe4 has great potential for the design of room-temperature efficient spintronic devices.

cond-mat.mtrl-sci↗
arXiv2024

Surface molecular engineering to enable processing of sulfide solid electrolytes in humid ambient air

Sulfide solid state electrolytes are promising candidates to realize all solid state batteries due to their superior ionic conductivity and excellent ductility. However, their hypersensitivity to moisture requires processing environments that are not compatible with todays lithium ion battery manufacturing infrastructure. Herein, we present a reversible surface modification strategy that enables the processability of sulfide SSEs under humid ambient air. We demonstrate that a long chain alkyl thiol, undecanethiol, is chemically compatible with the electrolyte with negligible impact on its ion conductivity. Importantly, the thiol modification extends the amount of time that the sulfide SSE can be exposed to air with 33 percent relative humidity with limited degradation of its structure while retaining a conductivity of above 1 mS per cm for up to 2 days, a more than 100 fold improvement in protection time over competing approaches. Experimental and computational results reveal that the thiol group anchors to the SSE surface, while the hydrophobic hydrocarbon tail provides protection by repelling water. The modified Li6PS5Cl SSE maintains its function after exposure to ambient humidity when implemented in a Li0.5In LiNi0.8Co0.1Mn0.1O2 ASSB. The proposed protection strategy based on surface molecular interactions represents a major step forward towards cost competitive and energy efficient sulfide SSE manufacturing for ASSB applications.

cond-mat.mtrl-sci↗
arXiv2024

RIGI: Rectifying Image-to-3D Generation Inconsistency via Uncertainty-aware Learning

Given a single image of a target object, image-to-3D generation aims to reconstruct its texture and geometric shape. Recent methods often utilize intermediate media, such as multi-view images or videos, to bridge the gap between input image and the 3D target, thereby guiding the generation of both shape and texture. However, inconsistencies in the generated multi-view snapshots frequently introduce noise and artifacts along object boundaries, undermining the 3D reconstruction process. To address this challenge, we leverage 3D Gaussian Splatting (3DGS) for 3D reconstruction, and explicitly integrate uncertainty-aware learning into the reconstruction process. By capturing the stochasticity between two Gaussian models, we estimate an uncertainty map, which is subsequently used for uncertainty-aware regularization to rectify the impact of inconsistencies. Specifically, we optimize both Gaussian models simultaneously, calculating the uncertainty map by evaluating the discrepancies between rendered images from identical viewpoints. Based on the uncertainty map, we apply adaptive pixel-wise loss weighting to regularize the models, reducing reconstruction intensity in high-uncertainty regions. This approach dynamically detects and mitigates conflicts in multi-view labels, leading to smoother results and effectively reducing artifacts. Extensive experiments show the effectiveness of our method in improving 3D generation quality by reducing inconsistencies and artifacts.

cs.CV↗
arXiv2024

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing

Point cloud processing (PCP) encompasses tasks like reconstruction, denoising, registration, and segmentation, each often requiring specialized models to address unique task characteristics. While in-context learning (ICL) has shown promise across tasks by using a single model with task-specific demonstration prompts, its application to PCP reveals significant limitations. We identify inter-task and intra-task sensitivity issues in current ICL methods for PCP, which we attribute to inflexible sampling strategies lacking context adaptation at the point and prompt levels. To address these challenges, we propose MICAS, an advanced ICL framework featuring a multi-grained adaptive sampling mechanism tailored for PCP. MICAS introduces two core components: task-adaptive point sampling, which leverages inter-task cues for point-level sampling, and query-specific prompt sampling, which selects optimal prompts per query to mitigate intra-task sensitivity. To our knowledge, this is the first approach to introduce adaptive sampling tailored to the unique requirements of point clouds within an ICL framework. Extensive experiments show that MICAS not only efficiently handles various PCP tasks but also significantly outperforms existing methods. Notably, it achieves a remarkable $4.1\%$ improvement in the part segmentation task and delivers consistent gains across various PCP applications.

cs.CV↗
arXiv2024

Performance Boundaries and Tradeoffs in Super-Resolution Imaging Technologies for Space Targets

Inverse synthetic aperture radar (ISAR) super-resolution imaging technology is widely applied in space target imaging. However, the performance limits of super-resolution imaging algorithms remain a rarely explored issue. This paper investigates these limits by analyzing the boundaries of super-resolution algorithms for space targets and examines the relationships between key contributing factors. In particular, drawing on the established mathematical theory of computational resolution limits (CRL) for line spectrum reconstruction, we derive mathematical expressions for the upper and lower bounds of cross-range super-resolution imaging, based on ISAR imaging model transformations. Leveraging the explicit expressions, we first explore influencing factors of these bounds, such as the traditional Rayleigh limit, the number of scatterers, and the peak signal-to-noise ratio (PSNR) of scatterers. Then we elucidate the minimum resource requirements in ISAR imaging imposed by the CRL theory to meet the desired cross-range resolution, without which studying super-resolution algorithms becomes unnecessary in practice. Furthermore, the tradeoffs between the cumulative rotation angle, the radar transmit energy, and other contributing factors in optimizing the resolution are analyzed. Simulations are conducted to demonstrate these tradeoffs across various ISAR imaging scenarios, revealing their high dependence on specific imaging targets.

eess.SP↗
arXiv2024

DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation

Dataset distillation is an advanced technique aimed at compressing datasets into significantly smaller counterparts, while preserving formidable training performance. Significant efforts have been devoted to promote evaluation accuracy under limited compression ratio while overlooked the robustness of distilled dataset. In this work, we introduce a comprehensive benchmark that, to the best of our knowledge, is the most extensive to date for evaluating the adversarial robustness of distilled datasets in a unified way. Our benchmark significantly expands upon prior efforts by incorporating a wider range of dataset distillation methods, including the latest advancements such as TESLA and SRe2L, a diverse array of adversarial attack methods, and evaluations across a broader and more extensive collection of datasets such as ImageNet-1K. Moreover, we assessed the robustness of these distilled datasets against representative adversarial attack algorithms like PGD and AutoAttack, while exploring their resilience from a frequency perspective. We also discovered that incorporating distilled data into the training batches of the original dataset can yield to improvement of robustness.

cs.CV↗
arXiv2024

Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching

Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have raised growing concerns. As one of the most promising directions, graph condensation methods address these issues by employing gradient matching, aiming to condense the full graph into a more concise yet information-rich synthetic set. Though encouraging, these strategies primarily emphasize matching directions of the gradients, which leads to deviations in the training trajectories. Such deviations are further magnified by the differences between the condensation and evaluation phases, culminating in accumulated errors, which detrimentally affect the performance of the condensed graphs. In light of this, we propose a novel graph condensation method named \textbf{C}raf\textbf{T}ing \textbf{R}ationa\textbf{L} trajectory (\textbf{CTRL}), which offers an optimized starting point closer to the original dataset's feature distribution and a more refined strategy for gradient matching. Theoretically, CTRL can effectively neutralize the impact of accumulated errors on the performance of condensed graphs. We provide extensive experiments on various graph datasets and downstream tasks to support the effectiveness of CTRL. Code is released at https://github.com/NUS-HPC-AI-Lab/CTRL.

cs.LG↗
arXiv2024

Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition

Human Activity Recognition (HAR) has gained great attention from researchers due to the popularity of mobile devices and the need to observe users' daily activity data for better human-computer interaction. In this work, we collect a human activity recognition dataset called OPPOHAR consisting of phone IMU data. To facilitate the employment of HAR system in mobile phone and to achieve user-specific activity recognition, we propose a novel light-weight network called Non-stationary BERT with a two-stage training method. We also propose a simple yet effective data augmentation method to explore the deeper relationship between the accelerator and gyroscope data from the IMU. The network achieves the state-of-the-art performance testing on various activity recognition datasets and the data augmentation method demonstrates its wide applicability.

cs.AI↗
arXiv2024

XRL: An FMM-Accelerated SIE Simulator for Resistance and Inductance Extraction of Complicated 3-D Geometries

A fast multipole method (FMM)-accelerated surface integral equation (SIE) simulator, called XRL, is proposed for broadband resistance/inductance (RL) extraction under the magneto-quasi-static assumption. The proposed XRL has three key attributes that make it highly efficient and accurate for broadband RL extraction of complicated 3-D geometries: (i) The XRL leverages a novel centroid-midpoint basis transformation while discretizing surface currents, which allows converting edge-based vector potential computations to panel-based scalar potential computations. Such conversion makes the implementation of FMM straightforward and allows for drastically reducing the memory and computational time requirements of the simulator. (ii) The XRL employs a highly accurate equivalent surface impedance model that allows extracting RL parameters at low frequencies very accurately. (iii) The XRL makes use of a novel preconditioner, effectively including both diagonal entries and some near-field entries of the system matrix; such preconditioner significantly accelerates the iterative solution of SIE. The proposed XRL can accurately compute broadband RL parameters of arbitrarily shaped and large-scale structures on a desktop computer. It has been applied to RL parameter extraction of various practical structures, including two parallel square coils, a ball grid array (BGA) package and a high brand package on package. Its application to the parameter extraction of the BGA shows that the XRL requires 93.2x and 14.2x less computational time and memory resources compared to the commercial simulator Ansys Q3D for the same level of accuracy, respectively.

eess.SP↗
arXiv2024

Generalised Brillouin Zone for Non-Reciprocal Systems

Recently, it has been observed that the Floquet-Bloch transform with real quasiperiodicities fails to capture the spectral properties of non-reciprocal systems. The aim of this paper is to introduce the notion of a generalised Brillouin zone by allowing the quasiperiodicities to be complex in order to rectify this. It is proved that this shift of the Brillouin zone into the complex plane accounts for the unidirectional spatial decay of the eigenmodes and leads to correct spectral convergence properties. The results in this paper clarify and prove rigorously how the spectral properties of a finite structure are associated with those of the corresponding semi-infinitely or infinitely periodic lattices and give explicit characterisations of how to extend the Hermitian theory to non-reciprocal settings. Based on our theory, we characterise the generalised Brillouin zone for both open boundary conditions and periodic boundary conditions. Our results are consistent with the physical literature and give explicit generalisations to the $k$-Toeplitz matrix cases.

math-ph↗
arXiv2024

Superionic surface Li-ion transport in carbonaceous materials

Unlike Li-ion transport in the bulk of carbonaceous materials, little is known about Li-ion diffusion on their surface. In this study, we have discovered an ultra-fast Li-ion transport phenomenon on the surface of carbonaceous materials, particularly when they have limited Li insertion capacity along with a high surface area. This is exemplified by a carbon black, Ketjen Black (KB). An ionic conductivity of 18.1 mS cm-1 at room temperature is observed, far exceeding most solid-state ion conductors. Theoretical calculations reveal a low diffusion barrier for the surface Li species. The species is also identified as Li*, which features a partial positive charge. As a result, lithiated KB functions effectively as an interlayer between Li and solid-state electrolytes (SSE) to mitigate dendrite growth and cell shorting. This function is found to be electrolyte agnostic, effective for both sulfide and halide SSEs. Further, lithiated KB can act as a high-performance mixed ion/electron conductor that is thermodynamically stable at potentials near Li metal. A graphite anode mixed with KB instead of a solid electrolyte demonstrates full utilization with a capacity retention of ~85% over 300 cycles. The discovery of this surface-mediated ultra-fast Li-ion transport mechanism provides new directions for the design of solid-state ion conductors and solid-state batteries.

cond-mat.mtrl-sci↗
arXiv2024

Tunable Localisation in Parity-Time-Symmetric Resonator Arrays with Imaginary Gauge Potentials

The aim of this paper is to illustrate both analytically and numerically the interplay of two fundamentally distinct non-Hermitian mechanisms in a deep subwavelength regime. Considering a parity-time symmetric system of one-dimensional subwavelength resonators equipped with two kinds of non-Hermiticity - an imaginary gauge potential and on-site gain and loss - we prove that all but two eigenmodes of the system decouple when going through an exceptional point. By tuning the gain-to-loss ratio, the system changes from a phase with unbroken parity-time symmetry to a phase with broken parity-time symmetry. At the macroscopic level, this is observed as a transition from symmetrical eigenmodes to condensated eigenmodes at one edge of the structure. Mathematically, it arises from a topological state change. The results of this paper open the door to the justification of a variety of phenomena arising from the interplay between non-Hermitian reciprocal and non-reciprocal mechanisms not only in subwavelength wave physics but also in quantum mechanics where the tight binding model coupled with the nearest neighbour approximation can be analysed with the same tools as those developed here.

physics.optics↗
arXiv2024

A mathematical theory of super-resolution and two-point resolution

This paper focuses on the fundamental aspects of super-resolution, particularly addressing the stability of super-resolution and the estimation of two-point resolution. Our first major contribution is the introduction of two location-amplitude identities that characterize the relationships between locations and amplitudes of true and recovered sources in the one-dimensional super-resolution problem. These identities facilitate direct derivations of the super-resolution capabilities for recovering the number, location, and amplitude of sources, significantly advancing existing estimations to levels of practical relevance. As a natural extension, we establish the stability of a specific $l_0$ minimization algorithm in the super-resolution problem. The second crucial contribution of this paper is the theoretical proof of a two-point resolution limit in multi-dimensional spaces. The resolution limit is expressed as: \[ R = \frac{4\arcsin \left(\left(\fracσ{m_{\min}}\right)^{\frac{1}{2}} \right)}Ω \] for $\fracσ{m_{\min}}\leq\frac{1}{2}$, where $\fracσ{m_{\min}}$ represents the inverse of the signal-to-noise ratio ($\mathrm{SNR}$) and $Ω$ is the cutoff frequency. It also demonstrates that for resolving two point sources, the resolution can exceed the Rayleigh limit $\fracπΩ$ when the signal-to-noise ratio (SNR) exceeds $2$. Moreover, we find a tractable algorithm that achieves the resolution $R$ when distinguishing two sources.

eess.IV↗