arXiv subjects

Hua Wang

Publications and source records attributed to Hua Wang.

At least 91 records · Page 5Linked to original sources

arXiv2025

Undamped Soliton-like Domain Wall Motion in Sliding Ferroelectrics

Sliding ferroelectricity in bilayer van der Waals materials exhibits ultrafast switching speed and fatigue resistance during the polarization switching, offering an avenue for the design of memories and neuromorphic devices. The unique polarization switching behavior originates from the distinct characteristics of domain wall (DW), which possesses broader width and faster motion compared to conventional ferroelectrics. Herein, using machine-learning-assisted molecular dynamics simulations and field theory analysis, we predict an undamped soliton-like DW motion in sliding ferroelectrics. It is found that the DW in sliding ferroelectric bilayer 3R-MoS2 exhibits uniformly accelerated motion under an external field, with its velocity ultimately reaches the relativistic-like limit due to continuous acceleration. Remarkably, the DW velocity remains constant even after the external field removal, completely deviating from the velocity breakdown observed in conventional ferroelectrics. This work provides opportunities for applications of sliding ferroelectrics in memory devices based on DW engineering.

cond-mat.mtrl-sci↗

arXiv2025

Quantum Geometric Origin of Strain-Induced Ferroelectric Phase Transitions

Strain-regulated ferroelectric (FE) materials have long attracted significant attention due to their diverse applications. While soft-phonon theory and the (pseudo) Jahn-Teller effect have achieved considerable success in providing phenomenological descriptions and general understanding, the detailed connection between these perspectives and their microscopic dependence on strain regulation remains unclear. Here, under the framework of density-functional perturbation theory (DFPT), we demonstrate that the Berry curvature of electron-phonon coupling (EPC) plays a pivotal role in the interatomic force matrix (IFM). A subsequent model analysis shows that external strain can reverse the polarity of the EPC Berry curvature in (quasi)-degenerate electronic subsystems through band inversion, thereby directly leading to phonon softening. The general theory is then applied to the BiOCl monolayer as a benchmark, which offers an accurate description of the density functional theory (DFT) calculations. This mechanism is further observed across a broad range of materials through ab initio calculations, providing an insightful perspective on EPC quantum geometry in lattice dynamics and FE phase transitions.

cond-mat.mtrl-sci↗

arXiv2025

Quantum Geometric Origin of Strain-Tunable Giant Second-Harmonic Generation in Bi$_2$O$_2$X (X=S, Se, Te)

Two-dimensional (2D) materials with giant nonlinear optical (NLO) responses are essential for the development of advanced on-chip NLO devices. Using first-principles calculations, we predict a remarkable strain-induced enhancement of second-harmonic generation (SHG) in the high-performance 2D semiconductors Bi$_2$O$_2$X (X = S, Se, Te). The SHG susceptibilities of Bi$_2$O$_2$X under strain are on the order of 1~nm/V, rivalling the highest values reported among 2D materials. This giant SHG response originates from gauge-invariant geometric quantities, including the quantum metric, shift vector, and triple phase product. The strain also induces a bandgap variation in Bi$_2$O$_2$X. Intriguingly, in Bi$_2$O$_2$Te, strain-induced bandgap tuning drives a transition from a semiconductor to a half-metal, and ultimately to a polar metal. Our findings present a unique platform that combines strain-tunable bandgap engineering with exceptional NLO properties, while also highlighting the crucial role of quantum geometry in enhancing SHG.

cond-mat.mtrl-sci↗

arXiv2025

Electrical Control of Exciton-Polariton Condensate Josephson Junctions via Exciton Stark Effect

We propose harnessing the tools of modern nano-fabrication to provide electrical control of exciton-polariton (EP) condensates. We develop the theory of a device based on the Josephson effect in which electric fields can be used to both switch between and monitor various dynamical modes. In particular, both the bias potential and the Josephson energy can be tuned electrically via the exciton component. We model the device by a Gross-Pitaevskii equation assuming that ideal EP condensates are established with well-balanced pumping and dissipation. We find that the EP condensates can be manipulated through degrees of freedom not easily accessible in other coherent quantum systems, and the dynamics of EP Josephson junctions are richer than that of the conventional superconducting junctions. The ability to control and monitor the condensate by both optical and electrical means allows new ways to study its physics not possible by either, alone.

cond-mat.mes-hall↗

arXiv2024

Highly Polarizable Semiconductors and Universal Origin of Ferroelectricity in Materials with a Litharge-Type Structural Unit

We discover that a large family of [Pb$_2$F$_2$]- and [Bi$_2$O$_2$]-based mixed-anion materials with a litharge-type structural unit are highly polarizable layered semiconductors on the edge of ferroelectricity. First-principles calculations demonstrate that in this family of materials, compounds as diverse as PbFBr, BiOCl, BiCuOSe, Bi$_2$OS$_2$, and Bi$_5$O$_4$S$_3$Cl exhibit static dielectric constants an order of magnitude higher than typical semiconductors. Additionally, they undergo a ferroelectric transition when subjected to a few percent of tensile strain. The ferroelectric transitions of these materials are found to have a universal origin in the strong cross-bandgap hybridization of the cation $p$ orbitals, enabled by the cation 6s$^2$ lone-pair electrons and the litharge-type structure of the [Pb$_2$F$_2$] and [Bi$_2$O$_2$] layers, as demonstrated by the strain-induced ferroelectric transition in the archetypal litharge $α$-PbO. These results establish materials with a litharge-type structural unit as a large and versatile family of highly polarizable layered semiconductors in proximity to ferroelectricity, offering vast opportunities for multifunctional materials design.

cond-mat.mtrl-sci↗

arXiv2024

Multilinear fractional maximal and integral operators with homogeneous kernels, Hardy--Littlewood--Sobolev and Olsen-type inequalities

Let $m\in \mathbb{N}$ and $0<α<mn$.In this paper, we will use the idea of Hedberg to reprove that the multilinear operators $\mathcal{T}_{Ω,α;m}$ and $\mathcal{M}_{Ω,α;m}$ are bounded from $L^{p_1}(\mathbb R^n)\times L^{p_2}(\mathbb R^n)\times\cdots\times L^{p_m}(\mathbb R^n)$ into $L^q(\mathbb R^n)$ provided that $\vecΩ=(Ω_1,Ω_2,\dots,Ω_m)\in L^s(\mathbf{S}^{n-1})$, $s'<p_1,p_2,\dots,p_m<n/α$, \begin{equation*} \frac{\,1\,}{p}=\frac{1}{p_1}+\frac{1}{p_2}+\cdots+\frac{1}{p_m} \quad \mbox{and} \quad \frac{\,1\,}{q}=\frac{\,1\,}{p}-\fracα{n}. \qquad (*) \end{equation*} We also prove that under the assumptions that $\vecΩ=(Ω_1,Ω_2,\dots,Ω_m)\in L^s(\mathbf{S}^{n-1})$, $s'\leq p_1,p_2,\dots,p_m<n/α$ and $(*)$, the multilinear operators $\mathcal{T}_{Ω,α;m}$ and $\mathcal{M}_{Ω,α;m}$ are bounded from $L^{p_1}(\mathbb R^n)\times L^{p_2}(\mathbb R^n)\times \cdots\times L^{p_m}(\mathbb R^n)$ into $L^{q,\infty}(\mathbb R^n)$, which are completely new. Moreover, we will use the idea of Adams to show that $\mathcal{T}_{Ω,α;m}$ and $\mathcal{M}_{Ω,α;m}$ are bounded from $L^{p_1,κ}(\mathbb R^n)\times L^{p_2,κ}(\mathbb R^n)\times \cdots\times L^{p_m,κ}(\mathbb R^n)$ into $L^{q,κ}(\mathbb R^n)$ whenever $s'<p_1,p_2,\dots,p_m<n/α$, $0<κ<1$, \begin{equation*} \frac{\,1\,}{p}=\frac{1}{p_1}+\frac{1}{p_2}+\cdots+\frac{1}{p_m} \quad \mbox{and} \quad \frac{\,1\,}{q}=\frac{\,1\,}{p}-\fracα{n(1-κ)},\qquad (**) \end{equation*} and also bounded from $L^{p_1,κ}(\mathbb R^n)\times L^{p_2,κ}(\mathbb R^n)\times \cdots\times L^{p_m,κ}(\mathbb R^n)$ into $WL^{q,κ}(\mathbb R^n)$ whenever $s'\leq p_1,p_2,\dots,p_m<n/α$, $0<κ<1$ and $(**)$.

math.CA↗

arXiv2024

Some new characterizations of BLO and Campanato spaces in the Schrödinger setting

Let us consider the Schrödinger operator $\mathcal{L}=-Δ+V$ on $\mathbb R^d$ with $d\geq3$, where $Δ$ is the Laplacian operator on $\mathbb R^d$ and the nonnegative potential $V$ belongs to certain reverse Hölder class $RH_s$ with $s\geq d/2$. In this paper, the authors first introduce two kinds of function spaces related to the Schrödinger operator $\mathcal{L}$. A real-valued function $f\in L^1_{\mathrm{loc}}(\mathbb R^d)$ belongs to the (BLO) space $\mathrm{BLO}_{ρ,θ}(\mathbb R^d)$ with $0\leqθ<\infty$ if \begin{equation*} \|f\|_{\mathrm{BLO}_{ρ,θ}} :=\sup_{\mathcal{Q}}\bigg(1+\frac{r}{ρ(x_0)}\bigg)^{-θ}\bigg(\frac{1}{|Q(x_0,r)|} \int_{Q(x_0,r)}\Big[f(x)-\underset{y\in\mathcal{Q}}{\mathrm{ess\,inf}}\,f(y)\Big]\,dx\bigg), \end{equation*} where the supremum is taken over all cubes $\mathcal{Q}=Q(x_0,r)$ in $\mathbb R^d$, $ρ(\cdot)$ is the critical radius function in the Schrödinger context. For $0<β<1$, a real-valued function $f\in L^1_{\mathrm{loc}}(\mathbb R^d)$ belongs to the (Campanato) space $\mathcal{C}^{β,\ast}_{ρ,θ}(\mathbb R^d)$ with $0\leqθ<\infty$ if \begin{equation*} \|f\|_{\mathcal{C}^{β,\ast}_{ρ,θ}} :=\sup_{\mathcal{B}}\bigg(1+\frac{r}{ρ(x_0)}\bigg)^{-θ} \bigg(\frac{1}{|B(x_0,r)|^{1+β/d}}\int_{B(x_0,r)}\Big[f(x)-\underset{y\in\mathcal{B}}{\mathrm{ess\,inf}}\,f(y)\Big]\,dx\bigg), \end{equation*} where the supremum is taken over all balls $\mathcal{B}=B(x_0,r)$ in $\mathbb R^d$. Then we establish the corresponding John--Nirenberg inequality suitable for the space $\mathrm{BLO}_{ρ,θ}(\mathbb R^d)$ with $0\leqθ<\infty$ and $d\geq3$. Moreover, we give some new characterizations of the BLO and Campanato spaces related to $\mathcal{L}$ on weighted Lebesgue spaces, which is the extension of some earlier results.

math.CA↗

arXiv2024

Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising

We investigate the construction of gradient-guided conditional diffusion models for reconstructing private images, focusing on the adversarial interplay between differential privacy noise and the denoising capabilities of diffusion models. While current gradient-based reconstruction methods struggle with high-resolution images due to computational complexity and prior knowledge requirements, we propose two novel methods that require minimal modifications to the diffusion model's generation process and eliminate the need for prior knowledge. Our approach leverages the strong image generation capabilities of diffusion models to reconstruct private images starting from randomly generated noise, even when a small amount of differentially private noise has been added to the gradients. We also conduct a comprehensive theoretical analysis of the impact of differential privacy noise on the quality of reconstructed images, revealing the relationship among noise magnitude, the architecture of attacked models, and the attacker's reconstruction capability. Additionally, extensive experiments validate the effectiveness of our proposed methods and the accuracy of our theoretical findings, suggesting new directions for privacy risk auditing using conditional diffusion models.

cs.CV↗

arXiv2024

Intercalation of Functional Materials with Phase Transitions for Neuromorphic Applications

Introducing foreign ions, atoms, or molecules into emerging functional materials is crucial for manipulating material physical properties and innovating device applications. The intercalation of emerging new materials can induce multiple intrinsic changes, such as charge doping, chemical bonding, and lattice expansion, which facilitates the exploration of structural phase transformations, the tuning of symmetry-breaking-related physics, and the creation of brain-inspired advanced devices. Moreover, incorporating various hosts and intercalants enables a series of crystal structures with a rich spectrum of characteristics, greatly expanding the scope and fundamental understanding of existing materials. Herein, we summarize the methods typically used for the intercalation of functional materials. We highlight recent progress in intercalation-based phase transitions and their emerging physics, i.e., ferroelectric, magnetic, insulator-metal, superconducting, and charge-density-wave phase transitions. We discuss prospective device applications for intercalation-based phase transitions, i.e., neuromorphic devices. Finally, we provide potential future research lines for promoting its further development.

physics.app-ph↗

arXiv2024

TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text

Current Retrieval-Augmented Generation (RAG) systems concatenate and process numerous retrieved document chunks for prefill which requires a large volume of computation, therefore leading to significant latency in time-to-first-token (TTFT). To reduce the computation overhead as well as TTFT, we introduce TurboRAG, a novel RAG system that redesigns the inference paradigm of the current RAG system by first pre-computing and storing the key-value (KV) caches of documents offline, and then directly retrieving the saved KV cache for prefill. Hence, online computation of KV caches is eliminated during inference. In addition, we provide a number of insights into the mask matrix and positional embedding mechanisms, plus fine-tune a pretrained language model to maintain model accuracy of TurboRAG. Our approach is applicable to most existing large language models and their applications without any requirement in modification of models and inference systems. Experimental results across a suite of RAG benchmarks demonstrate that TurboRAG reduces TTFT by up to 9.4x compared to the conventional RAG systems (on an average of 8.6x), but reserving comparable performance to the standard RAG systems.

cs.CV↗

arXiv2024

MooER: LLM-based Speech Recognition and Translation Models from Moore Threads

In this paper, we present MooER, a LLM-based large-scale automatic speech recognition (ASR) / automatic speech translation (AST) model of Moore Threads. A 5000h pseudo labeled dataset containing open source and self collected speech data is used for training. We achieve performance comparable to other open source models trained with up to hundreds of thousands of hours of labeled speech data. Meanwhile, experiments conducted on Covost2 Zh2en testset suggest that our model outperforms other open source Speech LLMs. A BLEU score of 25.2 can be obtained. The main contributions of this paper are summarized as follows. First, this paper presents a training strategy for encoders and LLMs on speech related tasks (including ASR and AST) using a small size of pseudo labeled data without any extra manual annotation and selection. Second, we release our ASR and AST models and plan to open-source our training code and strategy in the near future. Moreover, a model trained on 8wh scale training data is planned to be released later on.

cs.CL↗

arXiv2024

A theory of locally convex Hopf algebras

Using the completed inductive, projective and injective tensor products of Grothendieck for locally convex topological vector spaces, we develop a systematic theory of locally convex Hopf algebras with an emphasis on Pontryagin-type dualities. We describe how classical Hopf algebras, real and complex Lie groups, as well as compact and discrete quantum groups, can all give rise to natural examples of this theory in a variety of different ways. We also show that the space of all continuous functions on a topological group $ G $ whose topological structures are compactly generated has an $ \varepsilon $-Hopf algebra structure, and we can recover $ G $ fully as a topological group from this locally convex Hopf algebra. The latter is done via a generalization of Gelfand duality, which is of its own interest. Certain projective and inductive limits are also considered in this framework, and it is shown that how this can lead to examples seemingly outside of the framework of locally compact quantum groups in the sense of Kustermans-Vaes. As an illustration, we propose a version of the infinite quantum permutation group $ S^{+}_{\infty} $, the free orthogonal group $ O^{+}_{\infty} $, and the free unitary group $ U^{+}_{\infty} $ as certain strict inductive limits, all of which still retain a nice duality. Combined with our duality theory, this may be seen as an alternative tentative approach to the Kac program of developing a Pontryagin-type duality to a wider class, while at the same time, we include many more interesting examples of classical and quantum groups.

math.FA↗

arXiv2024

Microscale physics and macroscale convective heat transfer in supercritical fluids

Driven by fundamental thermodynamic efficiency considerations, an emerging trend in the energy and propulsion systems is that the working fluid operates at a pressure above the critical pressure. Energy transport is thus accompanied by dramatic and strongly nonlinear variations of thermophysical properties, which cause abnormal heat transfer behavior and non-ideal gas effects. This situation raises a crucial challenge for the heat exchanger and turbomachinery design, overall energy and exergy efficiency. We aim to provide a multi-scale overview of the flow and thermal behavior of fluid above the critical point: microscopic physics and macroscopic transport. Microscopic physics, i.e. near-critical thermophysical properties, phase transition and fluid dynamics, are introduced. A particular focus will be on the supercritical pseudo boiling process, which is a generalization of classical liquid-vapor phase transitions to non-equilibrium supercritical states. These new perspectives lead to a revised view of the state space. Further, recent results demonstrated the possibility of stable supercritical fluid interfaces without surface tension. On the macroscale, recent progress on the modeling of turbulent flow and convective heat transfer of supercritical fluids are summarized. Direct numerical simulation is able to offer insights into the physics of thermal fluids. We start with a description of fundamental fluid mechanics problems related to supercritical fluids. In addition, the heat transfer deterioration in supercritical fluids is found to be closely connected to the flow relaminarization by the non-uniform body-force. Finally, various modeling approaches such as recently developed advanced Reynolds-averaged turbulence modeling as well as machine-learning methods are summarized.

physics.flu-dyn↗

arXiv2024

Sliding Flexoelectricity in Two-Dimensional van der Waals Systems

Two-dimensional sliding ferroelectrics, with their unique stacking degrees of freedom, offer a different approach to manipulate polarization by interlayer sliding. Bending sliding ferroelectrics inevitably leads to interlayer sliding motion, thus altering stacking orders and polarization properties. Here, by using machine-learning force field, we investigate the effects of bending deformation on geometries, stackings, energies, and polarizations in sliding ferroelectric bilayer h-BN and 3R-MoS2. We predict that bent ferroelectric bilayer forms irreversible kinks instead of arc when the bending angle exceeds a critical value. We demonstrate that the kinks originate from the competition between bending energy and interlayer van der Waals energy. The kink contains a ferroelectric domain wall that reverses the polarization, effectively inducing a flexoelectric effect. We term this phenomenon "sliding flexoelectricity" to distinguish it from conventional strain-gradient-induced flexoelectricity.

cond-mat.mtrl-sci↗

arXiv2024

Phase engineering of giant second harmonic generation in Bi$_2$O$_2$Se

Two-dimensional (2D) materials with remarkable second-harmonic generation (SHG) hold promise for future on-chip nonlinear optics. Relevant materials with both giant SHG response and environmental stability are long-sought targets. Here, we demonstrate the enormous SHG from the phase engineering of a high-performance semiconductor, Bi$_2$O$_2$Se (BOS), under uniaxial strain. SHG signals captured in strained 20 nm-BOS films exceed those of NbOI$_2$ and NbOCl$_2$ of similar thickness by a factor of 10, and are four orders of magnitude higher than monolayer-MoS$_2$, resulting in a significant second-order nonlinear susceptibility on the order of 1 nm V$^{-1}$. Intriguingly, the strain enables continuous adjustment of the ferroelectric phase transition across room temperature. Consequently, an exceptionally large tunability of SHG, approximately six orders of magnitude, is achieved through strain or thermal modulation. This colossal SHG, originating from the geometric phase of Bloch wave functions and coupled with sensitive tunability through multiple approaches in this air-stable 2D semiconductor, opens new possibilities for designing chip-scale, switchable nonlinear optical devices.

cond-mat.mtrl-sci↗

arXiv2024

A Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation

Vessel trajectory clustering, a crucial component of the maritime intelligent transportation systems, provides valuable insights for applications such as anomaly detection and trajectory prediction. This paper presents a comprehensive survey of the most prevalent distance-based vessel trajectory clustering methods, which encompass two main steps: trajectory similarity measurement and clustering. Initially, we conducted a thorough literature review using relevant keywords to gather and summarize pertinent research papers and datasets. Then, this paper discussed the principal methods of data pre-processing that prepare data for further analysis. The survey progresses to detail the leading algorithms for measuring vessel trajectory similarity and the main clustering techniques used in the field today. Furthermore, the various applications of trajectory clustering within the maritime context are explored. Finally, the paper evaluates the effectiveness of different algorithm combinations and pre-processing methods through experimental analysis, focusing on their impact on the performance of distance-based trajectory clustering algorithms. The experimental results demonstrate the effectiveness of various trajectory clustering algorithms and notably highlight the significant improvements that trajectory compression techniques contribute to the efficiency and accuracy of trajectory clustering. This comprehensive approach ensures a deep understanding of current capabilities and future directions in vessel trajectory clustering.

eess.IV↗

arXiv2024

Geodesic nature and quantization of shift vector

We present the geodesic nature and quantization of geometric shift vector in quantum systems, with the parameter space defined by the Bloch momentum, using the Wilson loop approach. Our analysis extends to include bosonic phonon drag shift vectors with non-vertical transitions. We demonstrate that the gauge invariant shift vector can be quantized as integer values, analogous to the Euler characteristic based on the Gauss-Bonnet theorem for a manifold with a smooth boundary. We reveal intricate relationships among geometric quantities such as the shift vector, Berry curvature, and quantum metric. Our findings demonstrate that the loop integral of the shift vector in the quantized interband formula contributes to the non-quantized component of the trace of conductivity in the circular photogalvanic effect. The Wilson loop method facilitates first-principles calculations, providing insights in the geometric underpinnings of these interband gauge invariant quantities and shedding light on their nonlinear optical manifestations in real materials.

cond-mat.mtrl-sci↗

arXiv2024

Improving Large Models with Small models: Lower Costs and Better Performance

Pretrained large models (PLMs), such as ChatGPT, have demonstrated remarkable performance across diverse tasks. However, the significant computational requirements of PLMs have discouraged most product teams from running or fine-tuning them. In such cases, to harness the exceptional performance of PLMs, one must rely on expensive APIs, thereby exacerbating the economic burden. Despite the overall inferior performance of small models, in specific distributions, they can achieve comparable or even superior results. Consequently, some input can be processed exclusively by small models. On the other hand, certain tasks can be broken down into multiple subtasks, some of which can be completed without powerful capabilities. Under these circumstances, small models can handle the simple subtasks, allowing large models to focus on challenging subtasks, thus improving the performance. We propose Data Shunt$^+$ (DS$^+$), a general paradigm for collaboration of small and large models. DS$^+$ not only substantially reduces the cost associated with querying large models but also effectively improves large models' performance. For instance, ChatGPT achieves an accuracy of $94.43\%$ on Amazon Product sentiment analysis, and DS$^+$ achieves an accuracy of $95.64\%$, while the cost has been reduced to only $31.18\%$. Besides, experiments also prove that the proposed collaborative-based paradigm can better inject specific task knowledge into PLMs compared to fine-tuning.

cs.CL↗