arXiv subjects

Bing Yang

Publications and source records attributed to Bing Yang.

At least 19 recordsLinked to original sources

arXiv2026

A Fourth-order Conservative Adaptive Multiresolution Wavelet Upwind Scheme for Compressible Flows

A fourth-order conservative adaptive multiresolution average-interpolating wavelet upwind scheme is proposed for compressible flows governed by hyperbolic conservation laws. A family of asymmetric average-interpolating wavelets with upwind properties is constructed for conservative finite volume discretization, while symmetric average-interpolating wavelets are employed for multiresolution decomposition and reconstruction of physical variables in the adaptive procedure. Since both the conservative discretization and the adaptive multiresolution representation are constructed from cell-average quantities, the proposed scheme preserves strict conservation during both numerical evolution and adaptive cell redistribution. Unlike hybrid adaptive wavelet methods that use wavelets mainly for data compression and mesh adaptation, the present adaptive wavelet upwind scheme utilizes average-interpolating wavelet multiresolution approximation to reconstruct the interface values directly for numerical flux evaluation, thereby avoiding additional ghost-cell marking and reconstruction near coarse--fine mesh interfaces. The boundary variation diminishing reconstruction is incorporated at the finest resolution level to achieve non-oscillatory shock-capturing capability. Numerical tests demonstrate that the proposed scheme achieves the expected fourth-order accuracy, maintains conservation errors close to machine precision, and controls numerical errors around the prescribed threshold. The proposed method also sharply captures shock waves and contact discontinuities without spurious oscillations and resolves multiscale smooth structures through a sparse adaptive representation. These results indicate that the proposed scheme provides an efficient, conservative, and reliable approach for high-resolution simulations of compressible flows.

math.NA↗

arXiv2026

Statistics-governed dynamical scaling in interacting anyonic chains

Particle statistics impose fundamental constraints on nonequilibrium quantum dynamics, yet it remains an open question whether anyonic statistics can lead to emergent dynamical scaling beyond the conventional Bose-Fermi paradigm. Here we investigate the far-from-equilibrium many-body relaxation of anyons in a one-dimensional lattice, uncovering a statistics-governed, robust scaling behavior that deviates from standard Bose-Fermi limits. Based on large-scale numerical simulations and scaling analysis, we find that in the weakly interacting regime, anyonic statistics leads to emergent superdiffusive scaling in particle transport, while the entanglement entropy remains ballistic and is essentially insensitive to exchange statistics. The anomalous dynamics can be interpreted intuitively from the statistical-phase-induced quantum interference that suppresses coherent holon-doublon propagation; in contrast, the entanglement growth is dominated by its configurational component, which maintains ballistic spreading regardless of the statistical phase. Our results establish anyonic statistics as a distinct source of universal nonequilibrium dynamics beyond bosons and fermions, with direct relevance to current quantum simulation experiments.

cond-mat.quant-gas↗

arXiv2026

Towards $2+1$D quantum electrodynamics on a cold-atom quantum simulator

Cold atoms have become a powerful platform for quantum-simulating lattice gauge theories in higher spatial dimensions. However, such realizations have been restricted to the lowest possible truncations of the gauge field, which limit the connections one can make to lattice quantum electrodynamics. Here, we propose a feasible cold-atom quantum simulator of a $(2+1)$-dimensional U$(1)$ lattice gauge theory in a spin $S=1$ truncation, featuring dynamical matter and gauge fields. We derive a mapping of this theory onto a bosonic computational basis, stabilized by an emergent gauge-protection mechanism through quantum Zeno dynamics. The implementation is based on a single-species Bose--Hubbard model realized in a tilted optical superlattice. This approach requires only moderate experimental resources already available in current ultracold-atom platforms. Using infinite matrix product state simulations, we benchmark real-time dynamics under global quenches. The results demonstrate faithful evolution of the target gauge theory and robust preservation of the gauge constraints. Our work significantly advances the experimental prospects for simulating higher-dimensional lattice gauge theories using larger gauge-field truncations.

cond-mat.quant-gas↗

arXiv2026

MyGram: Modality-aware Graph Transformer with Global Distribution for Multi-modal Entity Alignment

Multi-modal entity alignment aims to identify equivalent entities between two multi-modal Knowledge graphs by integrating multi-modal data, such as images and text, to enrich the semantic representations of entities. However, existing methods may overlook the structural contextual information within each modality, making them vulnerable to interference from shallow features. To address these challenges, we propose MyGram, a modality-aware graph transformer with global distribution for multi-modal entity alignment. Specifically, we develop a modality diffusion learning module to capture deep structural contextual information within modalities and enable fine-grained multi-modal fusion. In addition, we introduce a Gram Loss that acts as a regularization constraint by minimizing the volume of a 4-dimensional parallelotope formed by multi-modal features, thereby achieving global distribution consistency across modalities. We conduct experiments on five public datasets. Results show that MyGram outperforms baseline models, achieving a maximum improvement of 4.8% in Hits@1 on FBDB15K, 9.9% on FBYG15K, and 4.3% on DBP15K.

cs.AI↗

arXiv2026

Scalable cold-atom quantum simulator of a $3+1$D U$(1)$ lattice gauge theory with dynamical matter

The stated overarching goal of the highly active field of quantum simulation of high-energy physics (HEP) is to achieve the capability to study \textit{ab-initio} real-time microscopic dynamics of $3+1$D quantum chromodynamics (QCD). However, existing experimental realizations and theoretical proposals for future ones have remained restricted to one or two spatial dimensions. Here, we take a big step towards this goal by proposing a concrete experimentally feasible scalable cold-atom quantum simulator of a U$(1)$ quantum link model of quantum electrodynamics (QED) in three spatial dimensions, employing \textit{linear gauge protection} to stabilize gauge invariance. Using tree tensor network simulations, we benchmark the performance of this quantum simulator through near- and far-from-equilibrium observables, showing excellent agreement with the ideal gauge theory. Additionally, we introduce a method for \textit{analog quantum error mitigation} that accounts for unwanted first-order tunneling processes, vastly improving agreement between quantum-simulator and ideal-gauge-theory results. Our findings pave the way towards realistic quantum simulators of $3+1$D lattice gauge theories that can probe regimes well beyond classical simulability.

cond-mat.quant-gas↗

arXiv2026

MacVQA: Adaptive Memory Allocation and Global Noise Filtering for Continual Visual Question Answering

Visual Question Answering (VQA) requires models to reason over multimodal information, combining visual and textual data. With the development of continual learning, significant progress has been made in retaining knowledge and adapting to new information in the VQA domain. However, current methods often struggle with balancing knowledge retention, adaptation, and robust feature representation. To address these challenges, we propose a novel framework with adaptive memory allocation and global noise filtering called MacVQA for visual question answering. MacVQA fuses visual and question information while filtering noise to ensure robust representations, and employs prototype-based memory allocation to optimize feature quality and memory usage. These designs enable MacVQA to balance knowledge acquisition, retention, and compositional generalization in continual VQA learning. Experiments on ten continual VQA tasks show that MacVQA outperforms existing baselines, achieving 43.38% average accuracy and 2.32% average forgetting on standard tasks, and 42.53% average accuracy and 3.60% average forgetting on novel composition tasks.

cs.CV↗

arXiv2026

Transfer-learned Kolosov-Muskhelishvili Informed Neural Networks for Fracture Mechanics

Physics-informed neural networks have been widely applied to solid mechanics problems. However, balancing the governing partial differential equations and boundary conditions remains challenging, particularly in fracture mechanics, where accurate predictions strongly depend on refined sampling near crack tips. To overcome these limitations, a Kolosov-Muskhelishvili informed neural network with Williams enrichment is developed in this study. Benefiting from the holomorphic representation, the governing equations are satisfied by construction, and only boundary points are required for training. Across a series of benchmark problems, the Kolosov-Muskhelishvili informed neural network shows excellent agreement with analytical and finite element method references, achieving average relative errors below 1\% and $R^2$ above 0.99 for both mode I and mode II loadings. Furthermore, three crack propagation criteria (maximum tangential stress, maximum energy release rate, and principle of local symmetry) are integrated into the framework using a transfer learning strategy to predict crack propagation directions. The predicted paths are nearly identical across all criteria, and the transfer learning strategy reduces the required training time by more than 70\%. Overall, the developed framework provides a unified, mesh-free, and physically consistent approach for accurate and efficient crack propagation analysis.

cs.CE↗

arXiv2025

KeenKT: Knowledge Mastery-State Disambiguation for Knowledge Tracing

Knowledge Tracing (KT) aims to dynamically model a student's mastery of knowledge concepts based on their historical learning interactions. Most current methods rely on single-point estimates, which cannot distinguish true ability from outburst or carelessness, creating ambiguity in judging mastery. To address this issue, we propose a Knowledge Mastery-State Disambiguation for Knowledge Tracing model (KeenKT), which represents a student's knowledge state at each interaction using a Normal-Inverse-Gaussian (NIG) distribution, thereby capturing the fluctuations in student learning behaviors. Furthermore, we design an NIG-distance-based attention mechanism to model the dynamic evolution of the knowledge state. In addition, we introduce a diffusion-based denoising reconstruction loss and a distributional contrastive learning loss to enhance the model's robustness. Extensive experiments on six public datasets demonstrate that KeenKT outperforms SOTA KT models in terms of prediction accuracy and sensitivity to behavioral fluctuations. The proposed method yields the maximum AUC improvement of 5.85% and the maximum ACC improvement of 6.89%.

cs.AI↗

arXiv2025

IPDnet2: an efficient and improved inter-channel phase difference estimation network for sound source localization

IPDnet is our recently proposed real-time sound source localization network. It employs alternating full-band and narrow-band (B)LSTMs to learn the full-band correlation and narrow-band extraction of DP-IPD, respectively, which achieves superior performance. However, processing narrow-band independently incurs high computational complexity and the limited scalability of LSTM layers constrains the localization accuracy. In this work, we extend IPDnet to IPDnet2, improving both localization accuracy and efficiency. IPDnet2 adapts the oSpatialNet as the backbone to enhance spatial cues extraction and provide superior scalability. Additionally, a simple yet effective frequency-time pooling mechanism is proposed to compress frequency and time resolutions and thus reduce computational cost, and meanwhile not losing localization capability. Experimental results show that IPDnet2 achieves comparable localization performance with IPDnet while only requiring less than 2\% of its computation cost. Moreover, the proposed network achieves state-of-the-art SSL performance by scaling up the model size while still maintaining relatively low complexity.

eess.AS↗

arXiv2025

A Nonlinear Low-rank Representation Model with Convolutional Neural Network for Imputing Water Quality Data

The integrity of Water Quality Data (WQD) is critical in environmental monitoring for scientific decision-making and ecological protection. However, water quality monitoring systems are often challenged by large amounts of missing data due to unavoidable problems such as sensor failures and communication delays, which further lead to water quality data becoming High-Dimensional and Sparse (HDS). Traditional data imputation methods are difficult to depict the potential dynamics and fail to capture the deep data features, resulting in unsatisfactory imputation performance. To effectively address the above issues, this paper proposes a Nonlinear Low-rank Representation model (NLR) with Convolutional Neural Networks (CNN) for imputing missing WQD, which utilizes CNNs to implement two ideas: a) fusing temporal features to model the temporal dependence of data between time slots, and b) Extracting nonlinear interactions and local patterns to mine higher-order relationships features and achieve deep fusion of multidimensional information. Experimental studies on three real water quality datasets demonstrate that the proposed model significantly outperforms existing state-of-the-art data imputation models in terms of estimation accuracy. It provides an effective approach for handling water quality monitoring data in complex dynamic environments.

cs.LG↗

arXiv2025

Mel-McNet: A Mel-Scale Framework for Online Multichannel Speech Enhancement

Online multichannel speech enhancement has been intensively studied recently. Though Mel-scale frequency is more matched with human auditory perception and computationally efficient than linear frequency, few works are implemented in a Mel-frequency domain. To this end, this work proposes a Mel-scale framework (namely Mel-McNet). It processes spectral and spatial information with two key components: an effective STFT-to-Mel module compressing multi-channel STFT features into Mel-frequency representations, and a modified McNet backbone directly operating in the Mel domain to generate enhanced LogMel spectra. The spectra can be directly fed to vocoders for waveform reconstruction or ASR systems for transcription. Experiments on CHiME-3 show that Mel-McNet can reduce computational complexity by 60% while maintaining comparable enhancement and ASR performance to the original McNet. Mel-McNet also outperforms other SOTA methods, verifying the potential of Mel-scale speech enhancement.

eess.AS↗

arXiv2025

Engineering long-range and multi-body interactions via global kinetic constraints

Long-range and multi-body interactions are crucial for quantum simulation and quantum computation. Yet, their practical realization using elementary pairwise interactions remains an outstanding challenge. We propose an experimental scheme based on the Bose-Hubbard system with a periodic driving of the on-site energy and global-range density-density interactions, a setup readily implementable via cold atoms in optical lattices with cavity-mediated interactions. Optimally chosen driving parameters can induce global kinetic constraints, where tunneling rates are selectively suppressed depending on the particle number imbalance between all even and odd sites. This mechanism, together with the flexible tunability of local tunneling rates, provides efficient implementation schemes of a family of global controlled gates for quantum computation. We illustrate this scheme for the $N$-qubit Toffoli gate, circumventing the need for a two-body gate decomposition, and elaborate on the efficient preparation of entangled many-body states.

quant-ph↗

arXiv2025

A Causal Convolutional Low-rank Representation Model for Imputation of Water Quality Data

The monitoring of water quality is a crucial part of environmental protection, and a large number of monitors are widely deployed to monitor water quality. Due to unavoidable factors such as data acquisition breakdowns, sensors and communication failures, water quality monitoring data suffers from missing values over time, resulting in High-Dimensional and Sparse (HDS) Water Quality Data (WQD). The simple and rough filling of the missing values leads to inaccurate results and affects the implementation of relevant measures. Therefore, this paper proposes a Causal convolutional Low-rank Representation (CLR) model for imputing missing WQD to improve the completeness of the WQD, which employs a two-fold idea: a) applying causal convolutional operation to consider the temporal dependence of the low-rank representation, thus incorporating temporal information to improve the imputation accuracy; and b) implementing a hyperparameters adaptation scheme to automatically adjust the best hyperparameters during model training, thereby reducing the tedious manual adjustment of hyper-parameters. Experimental studies on three real-world water quality datasets demonstrate that the proposed CLR model is superior to some of the existing state-of-the-art imputation models in terms of imputation accuracy and time cost, as well as indicating that the proposed model provides more reliable decision support for environmental monitoring.

cs.LG↗

arXiv2025

Water Quality Data Imputation via A Fast Latent Factorization of Tensors with PID-based Optimizer

Water quality data can supply a substantial decision support for water resources utilization and pollution prevention. However, there are numerous missing values in water quality data due to inescapable factors like sensor failure, thereby leading to biased result for hydrological analysis and failing to support environmental governance decision accurately. A Latent Factorization of Tensors (LFT) with Stochastic Gradient Descent (SGD) proves to be an efficient imputation method. However, a standard SGD-based LFT model commonly surfers from the slow convergence that impairs its efficiency. To tackle this issue, this paper proposes a Fast Latent Factorization of Tensors (FLFT) model. It constructs an adjusted instance error into SGD via leveraging a nonlinear PID controller to incorporates the past, current and future information of prediction error for improving convergence rate. Comparing with state-of-art models in real world datasets, the results of experiment indicate that the FLFT model achieves a better convergence rate and higher accuracy.

cs.LG↗

arXiv2025

Scalable Generation of Massive Schr\"odinger Cat States via Quantum Tunneling

Massive objects in spatial superposition may provide insights into the interplay between quantum mechanics and gravity. Cold atomic interferometers offer a promising platform due to extended matter-wave coherence times and precise controllability. However, high-mass spatial superpositions beyond single atoms have yet to be generated in such setups. Here, we report the scalable realization of high-mass spatial entanglement via quantum tunneling of ultracold atoms in optical lattices. We observe coherent tunneling of bound clusters, forming a composite object with a mass of 608~amu. Full control of the model parameters allows us to mitigate the usual suppression of tunneling with increasing mass. Furthermore, we construct an interferometer to certify the entanglement and use spatially distributed Schr\"{o}dinger cat states to perform quantum-enhanced measurements. These results establish an approach to generating and detecting massive superposition states relevant to studies of quantum gravity.

cond-mat.quant-gas↗

arXiv2024

Enhancement of piezoelectric response in V doped LiNbO3 films deposited by RF magnetron sputtering

LiNbO3 films doped with vanadium (V) were deposited using RF magnetron sputtering technique. To realize doping with a wider range of V concentration, a 30 mm V metal inlaid target asymmetrically embedded in the 150 mm lithium niobate target was used. The V concentration in the deposited films was a decreasing function of the distance from the V target. The V/Nb ratio in the film decreased from 0.155 to 0.024. Surface and inner morphology and structure, phase and element composition, microstructure, and ferroelectric properties of the undoped and V doped LiNbO3 films were studied. The measured maximal d33 constant of the LiNbVO film with V/Nb ratio of 0.07 was about three times higher than that of the undoped LiNbO3 film, 13.5 pC/N and 4.76 pC/N, respectively. The optimal composition in the deposition geometry used was within the V/Nb ratio range of 0.05 to 0.13. Undoped and V doped LiNbO3 thin films were used as bulk acoustic wave ultrasonic transducers deposited on stainless steel plates to generate longitudinal waves and compare their ultrasonic performance.

cond-mat.mtrl-sci↗

arXiv2024

STNet: Deep Audio-Visual Fusion Network for Robust Speaker Tracking

Audio-visual speaker tracking aims to determine the location of human targets in a scene using signals captured by a multi-sensor platform, whose accuracy and robustness can be improved by multi-modal fusion methods. Recently, several fusion methods have been proposed to model the correlation in multiple modalities. However, for the speaker tracking problem, the cross-modal interaction between audio and visual signals hasn't been well exploited. To this end, we present a novel Speaker Tracking Network (STNet) with a deep audio-visual fusion model in this work. We design a visual-guided acoustic measurement method to fuse heterogeneous cues in a unified localization space, which employs visual observations via a camera model to construct the enhanced acoustic map. For feature fusion, a cross-modal attention module is adopted to jointly model multi-modal contexts and interactions. The correlated information between audio and visual features is further interacted in the fusion model. Moreover, the STNet-based tracker is applied to multi-speaker cases by a quality-aware module, which evaluates the reliability of multi-modal observations to achieve robust tracking in complex scenarios. Experiments on the AV16.3 and CAV3D datasets show that the proposed STNet-based tracker outperforms uni-modal methods and state-of-the-art audio-visual speaker trackers.

cs.CV↗

arXiv2024

SigDLA: A Deep Learning Accelerator Extension for Signal Processing

Deep learning and signal processing are closely correlated in many IoT scenarios such as anomaly detection to empower intelligence of things. Many IoT processors utilize digital signal processors (DSPs) for signal processing and build deep learning frameworks on this basis. While deep learning is usually much more computing-intensive than signal processing, the computing efficiency of deep learning on DSPs is limited due to the lack of native hardware support. In this case, we present a contrary strategy and propose to enable signal processing on top of a classical deep learning accelerator (DLA). With the observation that irregular data patterns such as butterfly operations in FFT are the major barrier that hinders the deployment of signal processing on DLAs, we propose a programmable data shuffling fabric and have it inserted between the input buffer and computing array of DLAs such that the irregular data is reorganized and the processing is converted to be regular. With the online data shuffling, the proposed architecture, SigDLA, can adapt to various signal processing tasks without affecting the deep learning processing. Moreover, we build a reconfigurable computing array to suit the various data width requirements of both signal processing and deep learning. According to our experiments, SigDLA achieves an average performance speedup of 4.4$\times$, 1.4$\times$, and 1.52$\times$, and average energy reduction of 4.82$\times$, 3.27$\times$, and 2.15$\times$ compared to an embedded ARM processor with customized DSP instructions, a DSP processor, and an independent DSP-DLA architecture respectively with 17% more chip area over the original DLAs.

cs.AR↗