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Hao Wu

Publications and source records attributed to Hao Wu.

At least 433 records · Page 24Linked to original sources
arXiv2024

Global Weak Solutions to a Navier-Stokes-Cahn-Hilliard System with Chemotaxis and Mass Transport: Cross Diffusion versus Logistic Degradation

We analyze a diffuse interface model that describes the dynamics of incompressible two-phase flows influenced by interactions with a soluble chemical substance, encompassing the chemotaxis effect, mass transport, and reactions. In the resulting coupled evolutionary system, the macroscopic fluid velocity field $\boldsymbol{v}$ satisfies a Navier--Stokes system driven by a capillary force, the phase field variable $φ$ is governed by a convective Cahn--Hilliard equation incorporating a mass source and a singular potential (e.g., the Flory--Huggins type), and the chemical concentration $σ$ obeys an advection-reaction-diffusion equation with logistic degradation, exhibiting a cross-diffusion structure akin to the Keller--Segel model for chemotaxis. Under general structural assumptions, we establish the existence of global weak solutions to the initial boundary value problem within a bounded smooth domain $Ω\subset \mathbb{R}^d$, $d=2,3$. The proof hinges on a novel semi-Galerkin scheme for a suitably regularized system, featuring a non-standard approximation of the singular potential. Moreover, with more restrictive assumptions on coefficients and data, we establish regularity properties and uniqueness of global weak solutions in the two-dimensional case. Our analysis contributes to a further understanding of phase separation processes under the interplay of fluid dynamics and chemotaxis, in particular, the influence of cross diffusion and logistic degradation.

math.AP↗
arXiv2024

DIR: Retrieval-Augmented Image Captioning with Comprehensive Understanding

Image captioning models often suffer from performance degradation when applied to novel datasets, as they are typically trained on domain-specific data. To enhance generalization in out-of-domain scenarios, retrieval-augmented approaches have garnered increasing attention. However, current methods face two key challenges: (1) image features used for retrieval are often optimized based on ground-truth (GT) captions, which represent the image from a specific perspective and are influenced by annotator biases, and (2) they underutilize the full potential of retrieved text, typically relying on raw captions or parsed objects, which fail to capture the full semantic richness of the data. In this paper, we propose Dive Into Retrieval (DIR), a method designed to enhance both the image-to-text retrieval process and the utilization of retrieved text to achieve a more comprehensive understanding of the visual content. Our approach introduces two key innovations: (1) diffusion-guided retrieval enhancement, where a pretrained diffusion model guides image feature learning by reconstructing noisy images, allowing the model to capture more comprehensive and fine-grained visual information beyond standard annotated captions; and (2) a high-quality retrieval database, which provides comprehensive semantic information to enhance caption generation, especially in out-of-domain scenarios. Extensive experiments demonstrate that DIR not only maintains competitive in-domain performance but also significantly improves out-of-domain generalization, all without increasing inference costs.

cs.CV↗
arXiv2024

Lattice Vibration, Raman Modes and Room-Temperature Spin-Phonon Coupling in Intrinsic 2D van der Waals Ferromagnetic Fe3GaTe2

Two-dimensional (2D) van der Waals (vdW) magnets with spin-phonon coupling are crucial for next-generation spintronics. Among them, Fe3GaTe2 has attracted widespread attention due to above-room-temperature intrinsic ferromagnetism and large perpendicular magnetic anisotropy. However, the lattice vibrations and the interplay between ferromagnetism and lattice vibrations in Fe3GaTe2 are still unexplored. Here, we report the lattice vibration, Raman modes, and room-temperature spin-phonon coupling in 2D Fe3GaTe2 with above-room-temperature Curie temperature (TC). Two typical Raman modes with out-of-plane lattice vibrations are identified: "A" _"1g" ^"1" and "A" _"1g" ^"2" , whose frequencies increase as the thickness decreases from bulk to 2D Fe3GaTe2 due to the weakening of interlayer vdW interactions and spin exchange coupling. Moreover, the difference between phonon band dispersions under ferromagnetic and nonmagnetic interlayer spin ordering indicates the existence of spin-phonon coupling. The phonon frequency diverges from the anharmonic model below TC and thus the strength of spin-phonon coupling is ~0.28 cm-1 at 300 K, which is the first experimental identification of room-temperature spin-phonon coupling in 2D vdW magnets. This work deepens the understanding of novel 2D vdW magnets and provides a basis for spintronic applications at and above room temperature.

cond-mat.mtrl-sci↗
arXiv2024

GelSight FlexiRay: Breaking Planar Limits by Harnessing Large Deformations for Flexible,Full-Coverage Multimodal Sensing

The integration of tactile sensing into compliant soft robotic grippers offers a compelling pathway toward advanced robotic grasping and safer human-robot interactions. Visual-tactile sensors realize high-resolution, large-area tactile perception with affordable cameras. However, conventional visual-tactile sensors rely heavily on rigid forms, sacrificing finger compliance and sensing regions to achieve localized tactile feedback. Enabling seamless, large-area tactile sensing in soft grippers remains challenging, as deformations inherent to soft structures can obstruct the optical path and restrict the camera's field of view. To address these, we present Gelsight FlexiRay, a multimodal visual-tactile sensor designed for safe and compliant interactions with substantial structural deformation through integration with Finray Effect grippers. First, we adopt a multi-mirror configuration, which is systematically modeled and optimized based on the physical force-deformation characteristics of FRE grippers. Second, we enhanced Gelsight FlexiRay with human-like multimodal perception, including contact force and location, proprioception, temperature, texture, and slippage. Experiments demonstrate Gelsight FlexiRay's robust tactile performance across diverse deformation states, achieving a force measurement accuracy of 0.14 N and proprioceptive positioning accuracy of 0.19 mm. Compared with state of art compliant VTS, the FlexiRay demonstrates 5 times larger structural deformation under the same loads. Its expanded sensing area and ability to distinguish contact information and execute grasping and classification tasks highlights its potential for versatile, large-area multimodal tactile sensing integration within soft robotic systems. This work establishes a foundation for flexible, high-resolution tactile sensing in compliant robotic applications.

cs.RO↗
arXiv2024

CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks

Credit card fraud poses a significant threat to the economy. While Graph Neural Network (GNN)-based fraud detection methods perform well, they often overlook the causal effect of a node's local structure on predictions. This paper introduces a novel method for credit card fraud detection, the \textbf{\underline{Ca}}usal \textbf{\underline{T}}emporal \textbf{\underline{G}}raph \textbf{\underline{N}}eural \textbf{N}etwork (CaT-GNN), which leverages causal invariant learning to reveal inherent correlations within transaction data. By decomposing the problem into discovery and intervention phases, CaT-GNN identifies causal nodes within the transaction graph and applies a causal mixup strategy to enhance the model's robustness and interpretability. CaT-GNN consists of two key components: Causal-Inspector and Causal-Intervener. The Causal-Inspector utilizes attention weights in the temporal attention mechanism to identify causal and environment nodes without introducing additional parameters. Subsequently, the Causal-Intervener performs a causal mixup enhancement on environment nodes based on the set of nodes. Evaluated on three datasets, including a private financial dataset and two public datasets, CaT-GNN demonstrates superior performance over existing state-of-the-art methods. Our findings highlight the potential of integrating causal reasoning with graph neural networks to improve fraud detection capabilities in financial transactions.

cs.LG↗
arXiv2024

A Novel Flow-induced Motion Energy Harvesting with Coupled Mechanism of Time-varying Stiffness and Passive Turbulence Control

The ocean contains a substantial amount of energy, and the efficient harvesting of this energy holds significant importance. Drawing inspiration from the biomimicry of octopus tentacles, this study introduces a synergistic mechanism designed to optimize energy harvesting through flowinduced motions, integrating boundary layer modulation via passive turbulence control (PTC) with dynamic system stiffness adjustments via time-varying stiffness (SIN & Trapezoidal patterns). The implementation of PTC facilitates global stability by managing the local instabilities caused by variable stiffness, culminating in a highly effective energy harvesting capability. Our investigations summarize the requisite conditions for peak energy harvesting efficacy, notably within the SIN 80 degrees PTC and Trapezoid/60 degrees PTC arrangements, which have been demonstrated to double the efficiency of energy harvesting with up to 57%, alongside a reduction in initial harvesting frequency and an enhancement in both instantaneous power output and vibration amplitude. Furthermore, an energy transfer characteristic map has been compiled to illustrate the mechanism coupled between boundary layer modulation and time-varying stiffness. This research not only introduces novel perspectives but also stands as a significant stride in the realm of wide band and efficient energy harvesting in the ocean.

physics.flu-dyn↗
arXiv2024

Fast High-Quality Enhanced Imaging Algorithm for Layered Dielectric Targets Based on MMW MIMO-SAR System

Millimeter-wave (MMW) multiple-input multiple-output synthetic aperture radar (MIMO-SAR) system is a technology that can achieve high resolution, high frame rate, and all-weather imaging and has received extensive attention in the non-destructive testing and internal imaging applications of layered dielectric targets. However, the non-ideal scattering effect caused by dielectric materials can significantly deteriorate the imaging quality when using the existing MIMO-SAR fast algorithms. This paper proposes a rapid, high-quality dielectric target-enhanced imaging algorithm for a new universal non-uniform MIMO-SAR system. The algorithm builds on the existing non-uniform MIMO-SAR dielectric target frequency-domain algorithm (DT-FDA) by constructing a forward sensing operator and incorporating it into the alternating direction method of multipliers (ADMM) framework. This approach avoids large matrix operations while maintaining computational efficiency. By integrating an optimal regularization parameter search, the algorithm enhances the image reconstruction quality of dielectric internal structures or defects. Experimental results show the proposed algorithm outperforms IBP and DT-FDA, achieving better focusing, sidelobe suppression, and 3D imaging accuracy. It yields the lowest image entropy (8.864) and significantly improves efficiency (imaging time: 15.29 s vs. 23295.3 s for IBP).

eess.SP↗
arXiv2024

Deep-sub-cycle ultrafast optical pulses

Sub-cycle optical pulse is of great importance for ultrafast science and technology. While a narrower pulse can offer a higher temporal resolution, so far the pulse width has not reached the limit of half an optical cycle. Here we propose to break the half-cycle limit via inverse Compton scattering in a deep-subwavelength-confined infrared or THz optical driving field, which can be converted into a deep-sub-cycle (i.e., less than half a cycle) ultrafast pulse by relativistic electrons passing through. Quantitatively, by using a deep-subwavelength-confined 0.4-THz 1-ps pulsed driving field and a 3-MeV 1-fs electron bunch, we obtain a $\sim$ 0.1-cycle femtosecond pulse with a peak frequency of $\sim$ 26 THz ($\sim$ 38 fs in optical cycle) and a pulse width of $\sim$ 3.6 fs; with the same driving field and a 30-MeV 50-as electron bunch, we obtain a 0.17-cycle attosecond pulse with a peak frequency of $\sim$ 2.0 PHz ($\sim$ 500 as in optical cycle) and a pulse width of $\sim$ 86 as. Below the optical damage threshold of the field-confinement material, the single-pulse photon number can exceed $10^6$. Such pulses may open opportunities for studying light-matter interaction on the deep-sub-cycle level, and pave a way to unprecedented optical technology ranging from temporal super-resolution optical microscopy and spectroscopy to unconventional atom/molecule polarization and manipulation.

physics.optics↗
arXiv2024

Large critical fields in superconducting Ti$_{4}$Ir$_2$O from spin-orbit coupling

The recently synthesized $η$-carbide-type superconductors exhibit large critical fields. A notable example is Ti$_4$Ir$_2$O, for which the upper critical field strongly violates the Pauli paramagnetic limit, behavior that is unusual for cubic materials that preserve inversion symmetry. Here, by combining density functional theory (DFT) and analytic modeling, we provide an explanation for this enhanced Pauli limiting field. We show that the nonsymmorphic Fd$\overline{3}$m symmetry implies that the electronic states near the X points exhibit strong spin-orbit coupling (SOC), which leads to a vanishing effective $g$-factor and enables the enhanced Pauli limiting field. Furthermore, our DFT results reveal a Van Hove singularity (VHS) peak near the X points, accounting for $\sim$65\% of the total density of states (DOS), occurring near the chemical potential. We propose that the strong SOC and enhanced DOS in the vicinity of the X points provide the origin of the observed enhanced critical field. This leads to a prediction that the magnetic field will lead to a strongly momentum-dependent gap suppression. The gap due to electronic states away from (near to) the X points will be rapidly (slowly) suppressed by fields.

cond-mat.supr-con↗
arXiv2024

The LAMOST Spectroscopic Survey of Supergiants in M31 and M33

We present systematic identifications of supergiants of M31/M33 based on massive LAMOST spectroscopic survey. Radial velocities of nearly 5000 photometrically selected M31/M33 supergiant candidates have been properly derived from the qualified spectra released in LAMOST DR10. By comparing their radial velocities with those predicted from the rotation curves of M31, as well as utilizing {\it Gaia} astrometric measurements to exclude foreground contaminations, 199 supergiant members in M31, including 168 `Rank1' and 31 `Rank2', have been successfully identified. This sample contains 62 blue supergiants (BSGs, all `Rank1'), 134 yellow supergiants (YSGs, 103 `Rank1' and 31 `Rank2') and 3 red supergiants (RSGs, all `Rank1'). For M33, we identify 84 supergiant members (56 `Rank1' and 28 `Rank2'), which includes 28 BSGs (all `Rank1'), 53 YSGs (25 `Rank1' and 28 `Rank2') and 3 RSGs (all `Rank1'). So far, this is one of the largest supergiant sample of M31/M33 with full optical wavelength coverage (3700 \textless $λ$ \textless 9100 Å). This sample is valuable for understanding the star formation and stellar evolution under different environments.

astro-ph.GA↗
arXiv2024

DeepContext: A Context-aware, Cross-platform, and Cross-framework Tool for Performance Profiling and Analysis of Deep Learning Workloads

Effective performance profiling and analysis are essential for optimizing training and inference of deep learning models, especially given the growing complexity of heterogeneous computing environments. However, existing tools often lack the capability to provide comprehensive program context information and performance optimization insights for sophisticated interactions between CPUs and GPUs. This paper introduces DeepContext, a novel profiler that links program contexts across high-level Python code, deep learning frameworks, underlying libraries written in C/C++, as well as device code executed on GPUs. DeepContext incorporates measurements of both coarse- and fine-grained performance metrics for major deep learning frameworks, such as PyTorch and JAX, and is compatible with GPUs from both Nvidia and AMD, as well as various CPU architectures, including x86 and ARM. In addition, DeepContext integrates a novel GUI that allows users to quickly identify hotpots and an innovative automated performance analyzer that suggests users with potential optimizations based on performance metrics and program context. Through detailed use cases, we demonstrate how DeepContext can help users identify and analyze performance issues to enable quick and effective optimization of deep learning workloads. We believe Deep Context is a valuable tool for users seeking to optimize complex deep learning workflows across multiple compute environments.

cs.PF↗
arXiv2024

Privacy-Preserving Federated Learning via Dataset Distillation

Federated Learning (FL) allows users to share knowledge instead of raw data to train a model with high accuracy. Unfortunately, during the training, users lose control over the knowledge shared, which causes serious data privacy issues. We hold that users are only willing and need to share the essential knowledge to the training task to obtain the FL model with high accuracy. However, existing efforts cannot help users minimize the shared knowledge according to the user intention in the FL training procedure. This work proposes FLiP, which aims to bring the principle of least privilege (PoLP) to FL training. The key design of FLiP is applying elaborate information reduction on the training data through a local-global dataset distillation design. We measure the privacy performance through attribute inference and membership inference attacks. Extensive experiments show that FLiP strikes a good balance between model accuracy and privacy protection.

cs.LG↗
arXiv2024

FaaSTube: Optimizing GPU-oriented Data Transfer for Serverless Computing

Serverless computing has gained significant traction for machine learning inference applications, which are often deployed as serverless workflows consisting of multiple CPU and GPU functions with data dependency. However, existing data-passing solutions for serverless computing primarily reply on host memory for fast data transfer, mandating substantial data movement and resulting in salient I/O overhead. In this paper, we present FaaSTube, a GPU-efficient data passing system for serverless inference. FaaSTube manages intermediate data within a GPU memory pool to facilitate direct data exchange between GPU functions. It enables fine-grained bandwidth sharing over PCIe and NVLink, minimizing data-passing latency for both host-to-GPU and GPU-to-GPU while providing performance isolation between functions. Additionally, FaaSTube implements an elastic GPU memory pool that dynamically scales to accommodate varying data-passing demands. Evaluations on real-world applications show that FaaSTube reduces end-to-end latency by up to 90\% and achieves up to 12x higher throughput compared to the state-of-the-art.

cs.DC↗
arXiv2024

Random walk on dynamical percolation in Euclidean lattices: separating critical and supercritical regimes

We study the random walk on dynamical percolation of $\mathbb{Z}^d$ (resp., the two-dimensional triangular lattice $\mathcal{T}$), where each edge (resp., each site) can be either open or closed, refreshing its status at rate $μ\in (0,1/e]$. The random walk moves along open edges in $\mathbb{Z}^d$ (resp., open sites in $\mathcal{T}$) at rate $1$. For the critical regime $p=p_c$, we prove the following two results: on $\mathcal{T}$, the mean squared displacement of the random walk from $0$ to $t$ is at most $O(tμ^{5/132-ε})$ for any $ε>0$; on $\mathbb{Z}^d$ with $d\geq 11$, the corresponding upper bound for the mean squared displacement is $O(t μ^{1/2}\log(1/μ))$. For the supercritical regime $p>p_c$, we prove that the mean squared displacement on $\mathbb{Z}^d$ is at least $ct$ for some $c=c(d)>0$ that does not depend on $μ$.

math.PR↗
arXiv2024

A new quantum machine learning algorithm: split hidden quantum Markov model inspired by quantum conditional master equation

The Hidden Quantum Markov Model (HQMM) has significant potential for analyzing time-series data and studying stochastic processes in the quantum domain as an upgrading option with potential advantages over classical Markov models. In this paper, we introduced the split HQMM (SHQMM) for implementing the hidden quantum Markov process, utilizing the conditional master equation with a fine balance condition to demonstrate the interconnections among the internal states of the quantum system. The experimental results suggest that our model outperforms previous models in terms of scope of applications and robustness. Additionally, we establish a new learning algorithm to solve parameters in HQMM by relating the quantum conditional master equation to the HQMM. Finally, our study provides clear evidence that the quantum transport system can be considered a physical representation of HQMM. The SHQMM with accompanying algorithms present a novel method to analyze quantum systems and time series grounded in physical implementation.

quant-ph↗
arXiv2024

Computation and Critical Transitions of Rate-Distortion-Perception Functions With Wasserstein Barycenter

The information rate-distortion-perception (RDP) function characterizes the three-way trade-off between description rate, average distortion, and perceptual quality measured by discrepancy between probability distributions and has been applied to emerging areas in communications empowered by generative modeling. We study several variants of the RDP functions through the lens of optimal transport to characterize their critical transitions. By transforming the information RDP function into a Wasserstein Barycenter problem, we identify the critical transitions when one of the constraints becomes inactive. Further, the non-strictly convexity brought by the perceptual constraint can be regularized by an entropy regularization term. We prove that the entropy regularized model converges to the original problem and propose an alternating iteration method based on the Sinkhorn algorithm to numerically solve the regularized optimization problem. In many practical scenarios, the computation of the Distortion-Rate-Perception (DRP) function offers a solution to minimize distortion and perceptual discrepancy under rate constraints. However, the interchange of the rate objective and the distortion constraint significantly amplifies the complexity. The proposed method effectively addresses this complexity, providing an efficient solution for DRP functions. Using our numerical method, we propose a reverse data hiding scheme that imperceptibly embeds a secret message into an image, ensuring perceptual fidelity and achieving a significant improvement in the perceptual quality of the stego image compared to traditional methods under the same embedding rate. Our theoretical results and numerical method lay an attractive foundation for steganographic communications with perceptual quality constraints.

cs.IT↗
arXiv2024

Reliable and Compact Graph Fine-tuning via GraphSparse Prompting

Recently, graph prompt learning has garnered increasing attention in adapting pre-trained GNN models for downstream graph learning tasks. However, existing works generally conduct prompting over all graph elements (e.g., nodes, edges, node attributes, etc.), which is suboptimal and obviously redundant. To address this issue, we propose exploiting sparse representation theory for graph prompting and present Graph Sparse Prompting (GSP). GSP aims to adaptively and sparsely select the optimal elements (e.g., certain node attributes) to achieve compact prompting for downstream tasks. Specifically, we propose two kinds of GSP models, termed Graph Sparse Feature Prompting (GSFP) and Graph Sparse multi-Feature Prompting (GSmFP). Both GSFP and GSmFP provide a general scheme for tuning any specific pre-trained GNNs that can achieve attribute selection and compact prompt learning simultaneously. A simple yet effective algorithm has been designed for solving GSFP and GSmFP models. Experiments on 16 widely-used benchmark datasets validate the effectiveness and advantages of the proposed GSFPs.

cs.LG↗