arXiv subjects

Mei Wu

Publications and source records attributed to Mei Wu.

At least 19 recordsLinked to original sources

arXiv2026

US-VLA: An Ultrasound Vision-Language-Action Model for Embodied Abdomina

Artificial intelligence-assisted ultrasound scanning enhances diagnostic reliability and efficiency by providing real-time guidance for standardized image acquisition and reducing operator dependence. However, existing reinforcement learning and learning-assisted ultrasound scanning methods typically rely on carefully designed reward functions or extensive interaction data, which limits their generalization ability and stability across different devices, patient populations, and complex clinical scenarios. To address these challenges, we propose an ultrasound vision-language-action model (US-VLA) for automated ultrasound scanning that explicitly encodes clinical semantic goals and generates sequential probe manipulation actions under real-time ultrasound feedback. In particular, we first design an ultrasound-aware expert fusion module to jointly integrate ultrasound observations with auxiliary contextual information, enabling semantic ultrasound feedback to effectively guide the scanning process. Then, we construct US-VLA-Data, a real-world dataset covering liver and kidney examinations, which includes five clinically defined standard planes and comprises 320 expert scanning trajectories with approximately 80,000 synchronized timesteps. Extensive experiments demonstrate that US-VLA achieves competitive performance in ultrasound probe manipulation tasks, indicating its effectiveness and promising generalization within the evaluated abdominal ultrasound setting. The source code is available at https://github.com/VMVLab/US-VLA.

cs.RO↗

arXiv2026

CoMemNet: Contrastive Sampling with Memory Replay Network for Continual Traffic Prediction

In recent years, the integration of non-topological space modeling with temporal learning methods has emerged as an effective approach for capturing spatio-temporal information in non-Euclidean graphs. However, most existing methods rely on static underlying graph structures, which are inadequate for capturing the continuously expanding and evolving patterns in streaming traffic networks. To address this challenge, we propose a simple yet efficient dual-branch continual learning framework for traffic prediction, named CoMemNet. The fast-converging Online branch undertakes the primary prediction tasks, while the momentum-updated Target branch extracts historical information using Wasserstein Distance features to create a Dynamic Contrastive Sampler (DC Sampler). This sampler selects a node set with significant dynamic network feature changes for training, effectively mitigating the issue of catastrophic forgetting. Additionally, the backbone incorporates a lightweight Node-Adaptive Temporal Memory Buffer (TMRB-N) to consolidate old knowledge through memory replay and address the risk of memory explosion. Finally, we provide two newly curated open-source datasets. Experimental results demonstrate that CoMemNet achieves state-of-the-art (SOTA) performance across all three large-scale real-world datasets. The code is available at: https://github.com/meiwu5/CoMemNet.

cs.LG↗

arXiv2025

A Time-Enhanced Data Disentanglement Network for Traffic Flow Forecasting

In recent years, traffic flow prediction has become a highlight in the field of intelligent transportation systems. However, due to the temporal variations and dynamic spatial correlations of traffic data, traffic prediction remains highly challenging.Traditional spatiotemporal networks, which rely on end-to-end training, often struggle to handle the diverse data dependencies of multiple traffic flow patterns. Additionally, traffic flow variations are highly sensitive to temporal information changes. Regrettably, other researchers have not sufficiently recognized the importance of temporal information.To address these challenges, we propose a novel approach called A Time-Enhanced Data Disentanglement Network for Traffic Flow Forecasting (TEDDN). This network disentangles the originally complex and intertwined traffic data into stable patterns and trends. By flexibly learning temporal and node information through a dynamic graph enhanced by a temporal feature extraction module, TEDDN demonstrates significant efficacy in disentangling and extracting complex traffic information. Experimental evaluations and ablation studies on four real-world datasets validate the superiority of our method.

cs.AI↗

arXiv2025

MHGNet: Multi-Heterogeneous Graph Neural Network for Traffic Prediction

In recent years, traffic flow prediction has played a crucial role in the management of intelligent transportation systems. However, traditional forecasting methods often model non-Euclidean low-dimensional traffic data as a simple graph with single-type nodes and edges, failing to capture similar trends among nodes of the same type. To address this limitation, this paper proposes MHGNet, a novel framework for modeling spatiotemporal multi-heterogeneous graphs. Within this framework, the STD Module decouples single-pattern traffic data into multi-pattern traffic data through feature mappings of timestamp embedding matrices and node embedding matrices. Subsequently, the Node Clusterer leverages the Euclidean distance between nodes and different types of limit points to perform clustering with O(N) time complexity. The nodes within each cluster undergo residual subgraph convolution within the spatiotemporal fusion subgraphs generated by the DSTGG Module, followed by processing in the SIE Module for node repositioning and redistribution of weights. To validate the effectiveness of MHGNet, this paper conducts extensive ablation studies and quantitative evaluations on four widely used benchmarks, demonstrating its superior performance.

cs.LG↗

arXiv2025

SFADNet: Spatio-temporal Fused Graph based on Attention Decoupling Network for Traffic Prediction

In recent years, traffic flow prediction has played a crucial role in the management of intelligent transportation systems. However, traditional prediction methods are often limited by static spatial modeling, making it difficult to accurately capture the dynamic and complex relationships between time and space, thereby affecting prediction accuracy. This paper proposes an innovative traffic flow prediction network, SFADNet, which categorizes traffic flow into multiple traffic patterns based on temporal and spatial feature matrices. For each pattern, we construct an independent adaptive spatio-temporal fusion graph based on a cross-attention mechanism, employing residual graph convolution modules and time series modules to better capture dynamic spatio-temporal relationships under different fine-grained traffic patterns. Extensive experimental results demonstrate that SFADNet outperforms current state-of-the-art baselines across four large-scale datasets.

cs.LG↗

arXiv2024

Theoretical Investigation of (Zn, Co) co-Doped BaTiO3 for Advanced Energy and Photonic Applications

In light of recent advancements in energy technology, there is an urgent need for lead-free barium titanate (BTO) -based materials that exhibit remarkable ferroelectric and photoelectric properties. Notwithstanding the considerable experimental advances, a theoretical understanding from the electron and atomic perspectives remains elusive. This study employs the generalized gradient approximation plane wave pseudopotential technique to investigate the structural, electronic, ferroelectric, and optical properties of (Zn,Co) co-doped BaTiO3 (BZCT) based on density functional theory. The objective is to ascertain the extent of performance enhancement and the underlying mechanism of (Zn,Co) co-doping on barium titanate. Our findings reveal that incorporating (Zn,Co) into the BaTiO3 lattice significantly augments the tetragonality of the unit cell. Moreover, the ferroelectric properties are enhanced, with a spontaneous polarization stronger than that observed in pure BTO, exhibiting excellent ferroelectricity. The results of the Hubbard+U algorithm indicate that the band gap of BZCT is reduced. Concurrently, the enhanced ferroelectric polarization increases the built-in electric field of the material, facilitating the separation of photogenerated carriers and improving optical absorption. Consequently, the optical absorption ability and photorefractive ability are effectively enhanced. BZCT, with its high spontaneous polarization and outstanding optical properties, can be a promising candidate material in energy storage and photovoltaics.

cond-mat.mtrl-sci↗

arXiv2024

Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction

In recent years, deep learning has increasingly gained attention in the field of traffic prediction. Existing traffic prediction models often rely on GCNs or attention mechanisms with O(N^2) complexity to dynamically extract traffic node features, which lack efficiency and are not lightweight. Additionally, these models typically only utilize historical data for prediction, without considering the impact of the target information on the prediction. To address these issues, we propose a Pattern-Matching Dynamic Memory Network (PM-DMNet). PM-DMNet employs a novel dynamic memory network to capture traffic pattern features with only O(N) complexity, significantly reducing computational overhead while achieving excellent performance. The PM-DMNet also introduces two prediction methods: Recursive Multi-step Prediction (RMP) and Parallel Multi-step Prediction (PMP), which leverage the time features of the prediction targets to assist in the forecasting process. Furthermore, a transfer attention mechanism is integrated into PMP, transforming historical data features to better align with the predicted target states, thereby capturing trend changes more accurately and reducing errors. Extensive experiments demonstrate the superiority of the proposed model over existing benchmarks. The source codes are available at: https://github.com/wengwenchao123/PM-DMNet.

cs.LG↗

arXiv2024

Secure Combination of Untrusted Time information Based on Optimized Dempster-Shafer Theory

Secure precision time synchronization is important for applications of Cyber-Physical Systems. However, several attacks, especially the Time Delay Attack (TDA), deteriorates the performance of time synchronization system seriously. Multiple paths scheme is thought as an effective security countermeasure to decrease the influence of TDA. However, the effective secure combination algorithm is still missed for precision time synchronization. In this paper, a secure combination algorithm based on Dempster-Shafer theory is proposed for multiple paths method. Special optimizations are done for the combination algorithm to solve the potential problems due to untrusted evidence. Theoretical simulation shows that the proposed algorithm works much better than Fault Tolerant Algorithm (FTA) and the attack detection method based on single path. And experimental demonstration proves the feasibility and superiority of the proposed algorithm, where the time stability with 27.97 ps, 1.57 ps, and 1.12 ps at average time 1s, 10s, 100s is achieved under TDA and local clock jump. The proposed algorithm can be used to improve the security and resilience of many importance synchronization protocol, such as NTP, PTP, and TWFTT.

cs.CR↗

arXiv2023

An Ultra-fast Quantum Random Number Generation Scheme Based on Laser Phase Noise

Based on the intrinsic random property of quantum mechanics, quantum random number generators allow for access of truly unpredictable random sequence and are now heading towards high performance and small miniaturization, among which a popular scheme is based on the laser phase noise. However, this scheme is generally limited in speed and implementation complexity, especially for chip integration. In this work, a general physical model based on wiener process for such schemes is introduced, which provides an approach to clearly explain the limitation on the generation rate and comprehensively optimize the system performance. We present an insight to exploit the potential bandwidth of the quantum entropy source that contains plentiful quantum randomness with a simple spectral filtering method and experimentally boost the bandwidth of the corresponding quantum entropy source to 20 GHz, based on which an ultra-fast generation rate of 218 Gbps is demonstrated, setting a new record for laser phase noise based schemes by one order of magnitude. Our proposal significantly enhances the ceiling speed of such schemes without requiring extra complex hardware, thus effectively benefits the corresponding chip integration with high performance and low implementation cost, which paves the way for its large-scale applications.

quant-ph↗

arXiv2023

Reply to "Comment on newly found Charge Density Waves in infinite layer Nickelates''

Charge density waves (CDW) have been reported in NdNiO$_2$ and LaNiO$_2$ thin films grown on SrTiO$_3$ substrates using Ni-$L_3$ resonant x-ray scattering in Refs. [1-3]. In their comment [arXiv:2306.15086] on these reports, Pelliciari et al. found no evidence for a CDW in a NdNiO$_2$ film by performing fixed-momentum energy-dependent measurements. Instead, they observed a nearby non-resonant scattering peak, attributed to the (101) substrate reflection, made accessible at Ni-$L_3$ due to third harmonic light contamination. Here we present fixed-momentum energy-dependent resonant inelastic x-ray scattering measurements across Ni-$L_3$ on NdNiO$_2$, used in the preceding study [1]. We see intrinsic Ni-$L_3$ energy profiles at all measured \textbf{Q} values, including a strong resonance effect at $\mathbf{Q}_\mathrm{CDW} = (-1/3, 0, 0.316)$ reciprocal lattice units. Attempts to measure the (101) substrate peak using third harmonic light at Ni-$L_3$ at I21, Diamond were unfruitful. Our results clearly demonstrate the electronic origin of the scattering peak published in Ref. [1] and lack of a detectable structural component in the peak.

cond-mat.str-el↗

arXiv2022

Atomically localized ingredient-dependent interface phonon in heterogeneous solids

Phonons are the primary heat carriers in non-metallic solids. In compositionally heterogeneous materials, the thermal properties are believed to be mainly governed by the disrupted phonon transport due to mass disorder and strain fluctuations, while the effects of compositional fluctuation induced local phonon states are usually ignored. Here, by scanning transmission electron microscopy electron energy loss spectroscopy and sophisticated calculations, we identify the vibrational properties of ingredient-dependent interface phonon modes in AlxGa1-xN and quantify their various contributions to the local interface thermal conductance. We demonstrate that atomic-scale compositional fluctuation has significant influence on the vibrational thermodynamic properties, highly affecting the mode ratio and vibrational amplitude of interface phonon modes and subsequently redistributing their modal contribution to the ITC. Our work provides fundamental insights into understanding of local phonon-boundary interactions in nanoscale inhomogeneities, which reveal new opportunities for optimization of thermal properties via engineering ingredient distribution.

cond-mat.mtrl-sci↗

arXiv2022

An Insight-HXMT view of the mHz quasi-regular modulation phenomenon in the black hole X-ray binary 4U 1630-47

Here we report the spectral-timing results of the black hole X-ray binary 4U 1630-47 during its 2021 outburst using observations from the Hard X-ray Modulation Telescope. Type-C quasi-periodic oscillations (QPOs) in 1.6--4.2 Hz and quasi-regular modulation (QRM) near 60 mHz are detected during the outburst. The mHz QRM has a fractional rms of 10%--16% in the 8--35 keV energy band with a Q factor (frequency/width) of 2--4. Benefiting from the broad energy band of hxmt, we study the energy dependence of the 60 mHz QRM in 1--100 keV for the first time. We find that the fractional rms of the mHz QRM increases with photon energy, while the time lags of the mHz QRM are soft and decrease with photon energy. Fast recurrence of the mHz QRM, in a timescale of less than one hour, has been observed during the outburst. During this period, the corresponding energy spectra moderately change when the source transitions from the QRM state to the non-QRM state. The QRM phenomena also shows a dependence with the accretion rate. We suggest that the QRM could be caused by an unknown accretion instability aroused from the corona.

astro-ph.HE↗

arXiv2022

Electron microscopy probing electron-photon interactions in SiC nanowires with ultra-wide energy and momentum match

Nanoscale materials usually can trap light and strongly interact with it leading to many photonic device applications. The light-matter interactions are commonly probed by optical spectroscopy, which, however, have some limitations such as diffraction-limited spatial resolution, tiny momentum transfer and non-continuous excitation/detection. In this work, using scanning transmission electron microscopy-electron energy loss spectroscopy (STEM-EELS) with ultra-wide energy and momentum match and sub-nanometer spatial resolution, we study the optical microcavity resonant spectroscopy in a single SiC nanowire. The longitudinal Fabry-Perot (FP) resonating modes and the transverse whispering-gallery modes (WGMs) are simultaneously excited and detected, which span from near-infrared (~ 1.2 μm) to ultraviolet (~ 0.2 μm) spectral regime and the momentum transfer can be ranging up to 108 cm{^{-1}}. The size effects on the resonant spectra of nanowires are also revealed. Moreover, the nanoscale decay length of resonant EELS is revealed, which is contributed by the strongly localized electron-photon interactions in the SiC nanowire. This work provides a new alternative technique to investigate the optical resonating spectroscopy of a single nanowire structure and to explore the light-matter interactions in dielectric nanostructures, which is also promising for modulating free electrons via photonic structures.

cond-mat.mes-hall↗

arXiv2022

Quasi-periodic oscillations of the X-ray burst from the magnetar SGR J1935+2154 and associated with the fast radio burst FRB 200428

The origin(s) and mechanism(s) of fast radio bursts (FRBs), which are short radio pulses from cosmological distances, have remained a major puzzle since their discovery. We report a strong Quasi-Periodic Oscillation(QPO) of 40 Hz in the X-ray burst from the magnetar SGR J1935+2154 and associated with FRB 200428, significantly detected with the Hard X-ray Modulation Telescope (Insight-HXMT) and also hinted by the Konus-Wind data. QPOs from magnetar bursts have only been rarely detected; our 3.4 sigma (p-value is 2.9e-4) detection of the QPO reported here reveals the strongest QPO signal observed from magnetars (except in some very rare giant flares), making this X-ray burst unique among magnetar bursts. The two X-ray spikes coinciding with the two FRB pulses are also among the peaks of the QPO. Our results suggest that at least some FRBs are related to strong oscillation processes of neutron stars. We also show that we may overestimate the significance of the QPO signal and underestimate the errors of QPO parameters if QPO exists only in a fraction of the time series of a X-ray burst which we use to calculate the Leahy-normalized periodogram.

astro-ph.HE↗

arXiv2022

The First Insight-HXMT Gamma-Ray Burst Catalog: The First Four Years

The Hard X-ray Modulation Telescope (Insight-HXMT), is China's first X-ray astronomy satellite launched on June 15, 2017. The anti-coincidence CsI detectors of the High Energy X-ray telescope (HE) onboard Insight-HXMT could serve as an all-sky gamma-ray monitor in about 0.2-3 MeV. In its first four years of operation, Insight-HXMT has detected 322 Gamma-Ray Bursts (GRBs) by offline search pipeline including blind search and targeted search. For the GOLDEN sample of Insight-HXMT GRBs, joint analyses were performed with other GRB missions, including Fermi Gamma-ray Burst Monitor (Fermi/GBM), Swift Burst Alert Telescope (Swift/BAT) and Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM). It shows that Insight-HXMT can provide better constraint on GRB spectrum at higher energy band. The properties of Insight-HXMT GRBs are reported in detail, including their trigger time, duration, spectral parameters, peak fluxes of different time scales and fluence. This catalog is an official product of the Insight-HXMT GRB team.

astro-ph.HE↗

arXiv2021

Measuring phonon dispersion at an interface

The breakdown of translational symmetry at heterointerfaces leads to the emergence of new phonon modes localized near the interface. These interface phonons play an essential role in thermal/electrical transport properties in devices especially in miniature ones wherein the interface may dominate the entire response of the device. Knowledge of phonon dispersion at interfaces is therefore highly desirable for device design and optimization. Although theoretical work has begun decades ago, experimental research is totally absent due to challenges in achieving combined spatial, momentum and spectral resolutions required to probe localized phonon modes. Here we use electron energy loss spectroscopy in an electron microscope to directly measure both the local phonon density of states and the interface phonon dispersion relation for an epitaxial cBN-diamond heterointerface. In addition to bulk phonon modes, we observe acoustic and optical phonon modes localized at the interface, and modes isolated away from the interface. These features only appear within ~ 1 nm around the interface. The experimental results can be nicely reproduced by ab initio calculations. Our findings provide insights into lattice dynamics at heterointerfaces and should be practically useful in thermal/electrical engineering.

cond-mat.mtrl-sci↗

arXiv2021

Engineering of Atomic-Scale Flexoelectricity at Grain Boundaries

Flexoelectricity is a type of ubiquitous and prominent electromechanical coupling, pertaining to the response of electrical polarization to mechanical strain gradients while not restricted to the symmetry of materials. However, large elastic deformation in most solids is usually difficult to achieve and the strain gradient at minuscule is challenging to control. Here we exploit the exotic structural inhomogeneity of grain boundary to achieve a huge strain gradient (~ 1.2 nm-1) within 3 ~ 4 unit-cells, and thus obtain atomic-scale flexoelectric polarization up to ~ 38 μC/cm2 at a 24 LaAlO3 grain boundary. The nanoscale flexoelectricity also modifies the electrical activity of grain boundaries. Moreover, we prove that it is a general and feasible way to form large strain gradients at atomic scale by altering the misorientation angles of grain boundaries in different dielectric materials. Thus, engineering of grain boundaries provides an effective pathway to achieve tunable flexoelectricity and broadens the electromechanical functionalities of non-piezoelectric materials.

cond-mat.mtrl-sci↗

arXiv2021

Dynamics of Polar Skyrmion Bubbles under Electric Fields

Room-temperature polar skyrmion bubbles that are recently found in oxide superlattice, have received enormous interests for their potential applications in nanoelectronics due to the nanometer size, emergent chirality, and negative capacitance. For practical applications, the ability to controllably manipulate them by using external stimuli is prerequisite. Here, we study the dynamics of individual polar skyrmion bubbles at the nanoscale by using in situ biasing in a scanning transmission electron microscope. The reversible electric field-driven phase transition between topological and trivial polar states are demonstrated. We create, erase and monitor the shrinkage and expansion of individual polar skyrmions. We find that their transition behaviors are substantially different from that of magnetic analogue. The underlying mechanism is discussed by combing with the phase-field simulations. The controllable manipulation of nanoscale polar skyrmions allows us to tune the dielectric permittivity at atomic scale and detailed knowledge of their phase transition behaviors provides fundamentals for their applications in nanoelectronics.

cond-mat.mes-hall↗