arXiv subjects

Hailin Wang

Publications and source records attributed to Hailin Wang.

At least 19 recordsLinked to original sources

arXiv2026

Defining Unique, Redundant, and Synergistic Quantum Information

We extend the classical ideas of the Partial Information Decomposition (PID) to the quantum domain and quantify unique, redundant, and synergistic quantum information. We show that unique information plays the central role in quantum error correction codes: any erasure-correctable subset of encoding qubits must contain zero unique information. Synergistic information between two disjoint subsets of encoding qubits appears when a logical operation is supported on the whole set but not on the subsets separately. In a different application of our PID, we show that redundant quantum information is the crucial feature of the decoherence mechanism proposed by Zurek \textit{et al.} to explain how the classical world emerges out of the quantum world.

quant-ph↗

arXiv2026

Multi-Dimensional Visual Data Recovery: Scale-Aware Tensor Modeling and Accelerated Randomized Computation

The recently proposed fully-connected tensor network (FCTN) decomposition has demonstrated significant advantages in correlation characterization and transpositional invariance, and has achieved notable achievements in multi-dimensional data processing and analysis. However, existing multi-dimensional data recovery methods leveraging FCTN decomposition still have room for further enhancement, particularly in computational efficiency and modeling capability. To address these issues, we first propose a FCTN-based generalized nonconvex regularization paradigm from the perspective of gradient mapping. Then, reliable and scalable multi-dimensional data recovery models are investigated, where the model formulation is shifted from unquantized observations to coarse-grained quantized observations. Based on the alternating direction method of multipliers (ADMM) framework, we derive efficient optimization algorithms with convergence guarantees to solve the formulated models. To alleviate the computational bottleneck encountered when processing large-scale multi-dimensional data, fast and efficient randomized compression algorithms are devised in virtue of sketching techniques in numerical linear algebra. These dimensionality-reduction techniques serve as the computational acceleration core of our proposed algorithm framework. Theoretical results on approximation error upper bounds and convergence analysis for the proposed method are derived. Extensive numerical experiments illustrate the effectiveness and superiority of the proposed algorithm over other state-of-the-art methods in terms of quantitative metrics, visual quality, and running time.

cs.LG↗

arXiv2026

Decoupling effects of the resistive-switching behavior on the polarization reversal in ultrathin ferroelectric Hf0.5Zr0.5O2 films

HfO2-based ferroelectric films have attracted considerable attention as their nanoscale ferroelectricity and compatibility with cmos technology, fulfilling demands of emerging memory technologies. However, as films scale down, resistive-switching behavior becomes increasingly pronounced, intricately intertwining with the polarization-switching process and affecting ferroelectric switching factors often overlooked yet crucial for device performance optimization. By characterizing resistive-switching behavior and oxygen vacancy motion using tailored electric pulse schemes, we decouple the resistive-switching behavior from the overall switching process in ultrathin ferroelectric HZO films, which would otherwise erroneously inflate polarization values and increase coercive fields. Building on this, we elucidate endurance degradation mechanisms from dual perspectives of resistive switching and defect migration. Furthermore, we demonstrate the mitigated resistive switching activity by designing HfO2-based devices with symmetric oxide electrodes, achieving reduced coercive fields and improved cycling performances. This work provides crucial insights into the origins of inflated polarizations and reliability challenges in HfO2-based devices while offering a viable strategy to enhance ferroelectric properties for advanced memory applications.

cond-mat.mtrl-sci↗

arXiv2025

Hyperspectral Anomaly Detection Fused Unified Nonconvex Tensor Ring Factors Regularization

In recent years, tensor decomposition-based approaches for hyperspectral anomaly detection (HAD) have gained significant attention in the field of remote sensing. However, existing methods often fail to fully leverage both the global correlations and local smoothness of the background components in hyperspectral images (HSIs), which exist in both the spectral and spatial domains. This limitation results in suboptimal detection performance. To mitigate this critical issue, we put forward a novel HAD method named HAD-EUNTRFR, which incorporates an enhanced unified nonconvex tensor ring (TR) factors regularization. In the HAD-EUNTRFR framework, the raw HSIs are first decomposed into background and anomaly components. The TR decomposition is then employed to capture the spatial-spectral correlations within the background component. Additionally, we introduce a unified and efficient nonconvex regularizer, induced by tensor singular value decomposition (TSVD), to simultaneously encode the low-rankness and sparsity of the 3-D gradient TR factors into a unique concise form. The above characterization scheme enables the interpretable gradient TR factors to inherit the low-rankness and smoothness of the original background. To further enhance anomaly detection, we design a generalized nonconvex regularization term to exploit the group sparsity of the anomaly component. To solve the resulting doubly nonconvex model, we develop a highly efficient optimization algorithm based on the alternating direction method of multipliers (ADMM) framework. Experimental results on several benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art (SOTA) approaches in terms of detection accuracy.

cs.CV↗

arXiv2025

Accelerating Large-Scale Regularized High-Order Tensor Recovery

Currently, existing tensor recovery methods fail to recognize the impact of tensor scale variations on their structural characteristics. Furthermore, existing studies face prohibitive computational costs when dealing with large-scale high-order tensor data. To alleviate these issue, assisted by the Krylov subspace iteration, block Lanczos bidiagonalization process, and random projection strategies, this article first devises two fast and accurate randomized algorithms for low-rank tensor approximation (LRTA) problem. Theoretical bounds on the accuracy of the approximation error estimate are established. Next, we develop a novel generalized nonconvex modeling framework tailored to large-scale tensor recovery, in which a new regularization paradigm is exploited to achieve insightful prior representation for large-scale tensors. On the basis of the above, we further investigate new unified nonconvex models and efficient optimization algorithms, respectively, for several typical high-order tensor recovery tasks in unquantized and quantized situations. To render the proposed algorithms practical and efficient for large-scale tensor data, the proposed randomized LRTA schemes are integrated into their central and time-intensive computations. Finally, we conduct extensive experiments on various large-scale tensors, whose results demonstrate the practicability, effectiveness and superiority of the proposed method in comparison with some state-of-the-art approaches.

cs.LG↗

arXiv2025

A Pluggable Multi-Task Learning Framework for Sentiment-Aware Financial Relation Extraction

Relation Extraction (RE) aims to extract semantic relationships in texts from given entity pairs, and has achieved significant improvements. However, in different domains, the RE task can be influenced by various factors. For example, in the financial domain, sentiment can affect RE results, yet this factor has been overlooked by modern RE models. To address this gap, this paper proposes a Sentiment-aware-SDP-Enhanced-Module (SSDP-SEM), a multi-task learning approach for enhancing financial RE. Specifically, SSDP-SEM integrates the RE models with a pluggable auxiliary sentiment perception (ASP) task, enabling the RE models to concurrently navigate their attention weights with the text's sentiment. We first generate detailed sentiment tokens through a sentiment model and insert these tokens into an instance. Then, the ASP task focuses on capturing nuanced sentiment information through predicting the sentiment token positions, combining both sentiment insights and the Shortest Dependency Path (SDP) of syntactic information. Moreover, this work employs a sentiment attention information bottleneck regularization method to regulate the reasoning process. Our experiment integrates this auxiliary task with several prevalent frameworks, and the results demonstrate that most previous models benefit from the auxiliary task, thereby achieving better results. These findings highlight the importance of effectively leveraging sentiment in the financial RE task.

cs.CL↗

arXiv2025

Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation

Multi-label learning (MLL) has gained attention for its ability to represent real-world data. Label Distribution Learning (LDL), an extension of MLL to learning from label distributions, faces challenges in collecting accurate label distributions. To address the issue of biased annotations, based on the low-rank assumption, existing works recover true distributions from biased observations by exploring the label correlations. However, recent evidence shows that the label distribution tends to be full-rank, and naive apply of low-rank approximation on biased observation leads to inaccurate recovery and performance degradation. In this paper, we address the LDL with biased annotations problem from a novel perspective, where we first degenerate the soft label distribution into a hard multi-hot label and then recover the true label information for each instance. This idea stems from an insight that assigning hard multi-hot labels is often easier than assigning a soft label distribution, and it shows stronger immunity to noise disturbances, leading to smaller label bias. Moreover, assuming that the multi-label space for predicting label distributions is low-rank offers a more reasonable approach to capturing label correlations. Theoretical analysis and experiments confirm the effectiveness and robustness of our method on real-world datasets.

cs.LG↗

arXiv2025

Guaranteed Multidimensional Time Series Prediction via Deterministic Tensor Completion Theory

In recent years, the prediction of multidimensional time series data has become increasingly important due to its wide-ranging applications. Tensor-based prediction methods have gained attention for their ability to preserve the inherent structure of such data. However, existing approaches, such as tensor autoregression and tensor decomposition, often have consistently failed to provide clear assertions regarding the number of samples that can be exactly predicted. While matrix-based methods using nuclear norms address this limitation, their reliance on matrices limits accuracy and increases computational costs when handling multidimensional data. To overcome these challenges, we reformulate multidimensional time series prediction as a deterministic tensor completion problem and propose a novel theoretical framework. Specifically, we develop a deterministic tensor completion theory and introduce the Temporal Convolutional Tensor Nuclear Norm (TCTNN) model. By convolving the multidimensional time series along the temporal dimension and applying the tensor nuclear norm, our approach identifies the maximum forecast horizon for exact predictions. Additionally, TCTNN achieves superior performance in prediction accuracy and computational efficiency compared to existing methods across diverse real-world datasets, including climate temperature, network flow, and traffic ride data. Our implementation is publicly available at https://github.com/HaoShu2000/TCTNN.

cs.LG↗

arXiv2025

Fatigue-free ferroelectricity in Hf0.5Zr0.5O2 ultrathin films via interfacial design

Due to traits of CMOS compatibility and scalability, HfO2-based ferroelectrics are promising candidates for next-generation memory devices. However, their commercialization has been greatly hindered by reliability issues, with fatigue being a major impediment. We report the fatigue-free behavior in interface-designed Hf0.5Zr0.5O2-based heterostructures. A coherent CeO2-x/Hf0.5Zr0.5O2 heterointerface is constructed, wherein CeO2-x acts as an oxygen sponge, capable of reversibly accepting and releasing oxygen vacancies. This design effectively alleviates defect aggregation at the electrode-ferroelectric interface, enabling improved switching characteristics. Further, a symmetric capacitor architecture is designed to minimize the imprint, thereby suppressing the cycling-induced oriented defect drift. The two-pronged technique mitigates oxygen-voltammetry-generated chemical/energy fluctuations, suppressing the formation of paraelectric phase and polarization degradation. The design ensures a fatigue-free feature exceeding 10^11 switching cycles and an endurance lifetime surpassing 10^12 cycles for Hf0.5Zr0.5O2-based capacitors, along with excellent temperature stability and retention. These findings pave the way for developing ultra-stable hafnia-based ferroelectric devices.

cond-mat.mtrl-sci↗

arXiv2024

Energy level structure of diamond silicon vacancy centers in an off-axis magnetic field

We report the development of an experimental approach that can characterize the energy level structure of an individual silicon vacancy (SiV) center in a magnetic field and extract the key parameters for the energy level structure. This approach builds upon a theoretical model that includes effects of the static strain, dynamic Jahn-Teller coupling, and magnetic field and introduces two coupling rates, one each for the ground and the excited states, to characterize the combined effects of strain and Jahn-Teller coupling. With the use of an analytical expression for the energy level structure of the SiV center under a transverse magnetic field, these two coupling rates can be directly derived from the measurement of the frequency separation between two spin-conserved transitions in a photoluminescence excitation spectrum and the measurement of the coherent population trapping resonance related to the SiV ground spin states. Additional experimental studies on the dependence of the energy level structure on the magnetic field amplitude and direction further verify the theoretical model and reveal contributions from unequal orbital magnetic coupling for the ground and excited states.

cond-mat.mes-hall↗

arXiv2024

Mechanical Wannier-Stark Ladder of Diamond Spin-Mechanical Lamb Wave Resonators

We report the design and theoretical analysis of Wannier-Stark ladders of diamond Lamb wave resonators that feature mechanical compression modes with ultralow damping rates and host spin qubits with excellent optical and spin properties. The degree of localization in the mechanical Wannier-Stark ladder, which is determined by the ratio of coupling rate to frequency spacing between adjacent resonators, sets the effective range of phonon-mediated coupling between spin qubits. Three nearest-neighbor coupling schemes with distinct geometric configurations and a large range of coupling rates have been developed and analyzed. Additional analysis on the effects of disorder indicates that the proposed Wannier-Stark ladder can be robust against realistic experimental imperfections. The development of quantum networks of spin qubits with long-range connectivity can open the door to the implementation of newly developed quantum low-density parity-check codes in a solid-state system.

quant-ph↗

arXiv2024

Coherent population trapping and spin relaxation of a silicon vacancy center in diamond at mK temperatures

We report experimental studies of coherent population trapping and spin relaxation in a temperature range between 4 K and 100 mK in a silicon vacancy (SiV) center subject to a transverse magnetic field. Near and below 1 K, phonon-induced spin dephasing becomes negligible compared with that induced by the spin bath of naturally abundant 13C atoms. The temperature dependence of the spin dephasing rates agrees with the theoretical expectation that phonon-induced spin dephasing arises primarily from orbital relaxation induced by first order electron-phonon interactions. A nearly 100-fold increase in spin lifetime is observed when the temperature is lowered from 4 K to slightly below 1 K, indicating that two-phonon spin-flip transitions play an essential role in the spin relaxation of SiV ground states.

quant-ph↗

arXiv2024

Ultracoherent GHz Diamond Spin-Mechanical Lamb Wave Resonators

We report the development of an all-optical approach that excites the fundamental compression mode in a diamond Lamb wave resonator with an optical gradient force and detects the induced vibrations via strain coupling to a silicon vacancy center, specifically, via phonon sidebands in the optical excitation spectrum of the silicon vacancy. Sideband optical interferometry has also been used for the detection of the in-plane mechanical vibrations, for which conventional optical interferometry is not effective. These experiments demonstrate a GHz fundamental compression mode with a Q-factor >10^7 at temperatures near 7 K, providing a promising platform for reaching the quantum regime of spin mechanics, especially phononic cavity QED of electron spins.

quant-ph↗

arXiv2023

The block mutual coherence property condition for signal recovery

Compressed sensing shows that a sparse signal can stably be recovered from incomplete linear measurements. But, in practical applications, some signals have additional structure, where the nonzero elements arise in some blocks. We call such signals as block-sparse signals. In this paper, the $\ell_2/\ell_1-α\ell_2$ minimization method for the stable recovery of block-sparse signals is investigated. Sufficient conditions based on block mutual coherence property and associating upper bound estimations of error are established to ensure that block-sparse signals can be stably recovered in the presence of noise via the $\ell_2/\ell_1-α\ell_2$ minimization method. For all we know, it is the first block mutual coherence property condition of stably reconstructing block-sparse signals by the $\ell_2/\ell_1-α\ell_2$ minimization method. Additionally, the numerical experiments implemented verify the performance of the $\ell_2/\ell_1-α\ell_2$ minimization.

cs.IT↗

arXiv2023

Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation

Within the tensor singular value decomposition (T-SVD) framework, existing robust low-rank tensor completion approaches have made great achievements in various areas of science and engineering. Nevertheless, these methods involve the T-SVD based low-rank approximation, which suffers from high computational costs when dealing with large-scale tensor data. Moreover, most of them are only applicable to third-order tensors. Against these issues, in this article, two efficient low-rank tensor approximation approaches fusing randomized techniques are first devised under the order-d (d >= 3) T-SVD framework. On this basis, we then further investigate the robust high-order tensor completion (RHTC) problem, in which a double nonconvex model along with its corresponding fast optimization algorithms with convergence guarantees are developed. To the best of our knowledge, this is the first study to incorporate the randomized low-rank approximation into the RHTC problem. Empirical studies on large-scale synthetic and real tensor data illustrate that the proposed method outperforms other state-of-the-art approaches in terms of both computational efficiency and estimated precision.

cs.LG↗

arXiv2023

Mechanical photoluminescence excitation spectra of a strongly driven spin-mechanical system

We report experimental studies of a driven spin-mechanical system, in which a nitrogen vacancy (NV) center couples to out-of-plane vibrations of a diamond cantilever through the excited-state deformation potential. Photoluminescence excitation studies show that in the unresolved sideband regime and under strong resonant mechanical driving, the excitation spectra of a NV optical transition feature two spectrally sharp peaks, corresponding to the two turning points of the oscillating cantilever. In the limit that the strain-induced frequency separation between the two peaks far exceeds the NV zero-phonon linewidth, the spectral position of the individual peak becomes sensitive to minute detuning between the mechanical resonance and the external driving force. For a fixed optical excitation frequency near the NV transition, NV fluorescence as a function of mechanical detuning features resonances with a linewidth that can be orders of magnitude smaller than the intrinsic linewidth of the mechanical mode. This enhanced sensitivity to mechanical detuning can potentially provide an effective mechanism for mechanical sensing, for example, mass sensing via measurements of induced changes in the mechanical oscillator frequency.

cond-mat.mes-hall↗

arXiv2023

Guaranteed Tensor Recovery Fused Low-rankness and Smoothness

The tensor data recovery task has thus attracted much research attention in recent years. Solving such an ill-posed problem generally requires to explore intrinsic prior structures underlying tensor data, and formulate them as certain forms of regularization terms for guiding a sound estimate of the restored tensor. Recent research have made significant progress by adopting two insightful tensor priors, i.e., global low-rankness (L) and local smoothness (S) across different tensor modes, which are always encoded as a sum of two separate regularization terms into the recovery models. However, unlike the primary theoretical developments on low-rank tensor recovery, these joint L+S models have no theoretical exact-recovery guarantees yet, making the methods lack reliability in real practice. To this crucial issue, in this work, we build a unique regularization term, which essentially encodes both L and S priors of a tensor simultaneously. Especially, by equipping this single regularizer into the recovery models, we can rigorously prove the exact recovery guarantees for two typical tensor recovery tasks, i.e., tensor completion (TC) and tensor robust principal component analysis (TRPCA). To the best of our knowledge, this should be the first exact-recovery results among all related L+S methods for tensor recovery. Significant recovery accuracy improvements over many other SOTA methods in several TC and TRPCA tasks with various kinds of visual tensor data are observed in extensive experiments. Typically, our method achieves a workable performance when the missing rate is extremely large, e.g., 99.5%, for the color image inpainting task, while all its peers totally fail in such challenging case.

cs.LG↗

arXiv2022

VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges

Artificial Intelligence (AI) and its applications have sparked extraordinary interest in recent years. This achievement can be ascribed in part to advances in AI subfields including Machine Learning (ML), Computer Vision (CV), and Natural Language Processing (NLP). Deep learning, a sub-field of machine learning that employs artificial neural network concepts, has enabled the most rapid growth in these domains. The integration of vision and language has sparked a lot of attention as a result of this. The tasks have been created in such a way that they properly exemplify the concepts of deep learning. In this review paper, we provide a thorough and an extensive review of the state of the arts approaches, key models design principles and discuss existing datasets, methods, their problem formulation and evaluation measures for VQA and Visual reasoning tasks to understand vision and language representation learning. We also present some potential future paths in this field of research, with the hope that our study may generate new ideas and novel approaches to handle existing difficulties and develop new applications.

cs.CV↗