arXiv subjects

Jing Wu

Publications and source records attributed to Jing Wu.

At least 73 records · Page 4Linked to original sources

arXiv2025

Modica type estimates and curvature results for overdetermined $p$-Laplace problems

In this paper we prove Modica type estimates for the following overdetermined $p$-Laplace problem \begin{equation*} \begin{cases} \mathrm{div} \left(|\nabla u|^{p-2}\nabla u\right)+f(u) =0& \mbox{in $Ω$, } u>0 &\mbox{in $Ω$, } u=0 &\mbox{on $\partialΩ$, } \partial_ν u=-κ&\mbox{on $\partialΩ$, } \end{cases} \end{equation*} where $1 2$ we also assume that if $F(u_0)=0$, $F(u)=O(|u-u_0|^p)$ as $u\rightarrow u_0$), then either the mean curvature of $\partial Ω$ is strictly negative or $Ω$ is a half-space.

math.AP↗

arXiv2025

Random at First, Fast at Last: NTK-Guided Fourier Pre-Processing for Tabular DL

While random Fourier features are a classic tool in kernel methods, their utility as a pre-processing step for deep learning on tabular data has been largely overlooked. Motivated by shortcomings in tabular deep learning pipelines - revealed through Neural Tangent Kernel (NTK) analysis - we revisit and repurpose random Fourier mappings as a parameter-free, architecture-agnostic transformation. By projecting each input into a fixed feature space via sine and cosine projections with frequencies drawn once at initialization, this approach circumvents the need for ad hoc normalization or additional learnable embeddings. We show within the NTK framework that this mapping (i) bounds and conditions the network's initial NTK spectrum, and (ii) introduces a bias that shortens the optimization trajectory, thereby accelerating gradient-based training. These effects pre-condition the network with a stable kernel from the outset. Empirically, we demonstrate that deep networks trained on Fourier-transformed inputs converge more rapidly and consistently achieve strong final performance, often with fewer epochs and less hyperparameter tuning. Our findings establish random Fourier pre-processing as a theoretically motivated, plug-and-play enhancement for tabular deep learning.

cs.LG↗

arXiv2025

An Overdetermined Neumann boundary value problem with a general driving force

In this paper, we prove the existence of a family of non trivial compact subdomains $Ø$ in the manifold $\mathcal{M}=\R^N\times \R/2π\Z, N\geq 2$ for which the overdetermined Neumann boundary value problem \begin{align}\label{Neumann1} \left \{ \begin{aligned} $-\D w&=μg(w) && \qquad \text{in $ Ω$,}$ \frac{\partial w}{\partialη} &=0 &&\qquad \text{on $\partial Ω,$} w&=c\ne 0 &&\qquad \text{on $\partial Ω$,} \end{aligned} \right. \end{align} admits solutions for some $μ> 0$ and a $C^{1, α}$ function $g:\R \rightarrow \R.$ The domains we construct have nonconstant principal curvature, and therefore are not isoparametric nor homogeneous. The argument we develop applies for both linear and non-linear functions $g$. By this, we generalise a recent result obtained by Fall, Weth and the first named author in \cite{Fall-MinlendI-Weth4}, where the overdetermined Neumann eigenvalue problem for the Laplacian was considered.

math.AP↗

arXiv2025

Weyl Semimetals: from Principles, Materials to Applications

Weyl semimetals have attracted significant interest in condensed matter physics and materials science, due to their unique electronic and topological properties. These characteristics not only deepen our understanding of fundamental quantum phenomena, but also make Weyl semimetals promising candidates for advanced applications in electronics, photonics, and spintronics. This review provides a systematic overview of the field, covering theoretical foundations, material synthesis, engineering strategies, and emerging device applications. We first outline the key theoretical principles and distinctive properties of Weyl semimetals, followed by an examination of recent advancements that enhance their functional versatility. Finally, we discuss the critical challenges hindering their practical implementation and explore future development directions, along with the potential for expanding and enhancing their existing range of applications. By integrating discussions of both opportunities and obstacles, this review offers a balanced perspective on current progress and future directions in Weyl semimetal research.

cond-mat.mtrl-sci↗

arXiv2025

Advances in Bosonic Quantum Error Correction with Gottesman-Kitaev-Preskill Codes: Theory, Engineering and Applications

Encoding quantum information into a set of harmonic oscillators is considered a hardware efficient approach to mitigate noise for reliable quantum information processing. Various codes have been proposed to encode a qubit into an oscillator -- including cat codes, binomial codes and Gottesman-Kitaev-Preskill (GKP) codes -- and are among the first to reach a break-even point for quantum error correction. Though GKP codes are widely recognized for their promise in quantum computation, they also facilitate near-optimal quantum communication rates in bosonic channels and offer the ability to safeguard arbitrary quantum states of oscillators. This review focuses on the basic working mechanism, performance characterization, and the many applications of GKP codes -- emphasizing recent experimental progress in superconducting circuit architectures and theoretical advancements in multimode GKP qubit codes and oscillators-to-oscillators (O2O) codes. We begin with a preliminary continuous-variable formalism needed for bosonic codes. We then proceed to the quantum engineering involved to physically realize GKP states. We take a deep dive into GKP stabilization and preparation in superconducting architectures and examine proposals for realizing GKP states in the optical domain (along with a concise review of GKP realization in trapped-ion platforms). Finally, we present multimode GKP qubits and GKP-O2O codes, examine code performance and discuss applications of GKP codes in quantum information processing tasks such as computing, communication, and sensing.

quant-ph↗

arXiv2025

Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions

The rise of the low-altitude economy (LAE) is propelling urban development and emerging industries by integrating advanced technologies to enhance efficiency, safety, and sustainability in low-altitude operations. The widespread adoption of unmanned aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft plays a crucial role in enabling key applications within LAE, such as urban logistics, emergency rescue, and aerial mobility. However, unlike traditional UAV networks, LAE networks encounter increased airspace management demands due to dense flying nodes and potential interference with ground communication systems. In addition, there are heightened and extended security risks in real-time operations, particularly the vulnerability of low-altitude aircraft to cyberattacks from ground-based threats. To address these, this paper first explores related standards and core architecture that support the development of LAE networks. Subsequently, we highlight the integration of technologies such as communication, sensing, computing, positioning, navigation, surveillance, flight control, and airspace management. This synergy of multi-technology drives the advancement of real-world LAE applications, particularly in improving operational efficiency, optimizing airspace usage, and ensuring safety. Finally, we outline future research directions for LAE networks, such as intelligent and adaptive optimization, security and privacy protection, sustainable energy and power management, quantum-driven coordination, generative governance, and three-dimensional (3D) airspace coverage, which collectively underscore the potential of collaborative technologies to advance LAE networks.

cs.NI↗

arXiv2025

General Phase Segregation and Phase Pinning Effects in Ln-doped Lead Halide Perovskite with Dual-wavelength Lasing

Lead halide perovskites (LHPs) exhibit outstanding optoelectronic properties, making them highly promising for applications in various optoelectronics devices. However, rapid ion migration in LHPs not only undermines device stability but also hinders the development of multi-band composite structures, which are crucial to advancing perovskite bandgap engineering and unlocking novel applications. Here, we introduce a novel and general strategy involving both phase segregation and phase pinning by doping Er$^{3+}$ into CsPb(X$_x$Y$_{1-x}$)$_3$ (X, Y = Cl, Br, I) microplates via a simple one-step chemical vapor deposition method. The ion migration is effectively suppressed and a variety of stable multi-band composite structures are demonstrated, with diverse dual-band photoluminescence emissions covering the red, green, and blue spectral bands. The corresponding high-performance dual-wavelength lasers have also been fabricated, confirming the stability and high crystalline quality of these multi-band composite structures. In addition, this strategy is extended to the doping of various lanthanide ion and the incorporation of different mixed halides into LHPs. As a result, a series of multi-band composite structures based on LHPs and corresponding dual-wavelength lasers are developed, thereby validating the generality of this strategy. Theoretical calculations clarify the phase segregation and phase pinning mechanism in these LHPs. Our work not only facilitates the stability design of LHPs but also significantly advances the bandgap engineering, thereby contributing to the expansion of their potential applications in the optoelectronic future.

cond-mat.mtrl-sci↗

arXiv2025

Designing Large Foundation Models for Efficient Training and Inference: A Survey

This paper focuses on modern efficient training and inference technologies on foundation models and illustrates them from two perspectives: model and system design. Model and System Design optimize LLM training and inference from different aspects to save computational resources, making LLMs more efficient, affordable, and more accessible. The paper list repository is available at https://github.com/NoakLiu/Efficient-Foundation-Models-Survey.

cs.DC↗

arXiv2025

It Takes Two to Tango: Serverless Workflow Serving via Bilaterally Engaged Resource Adaptation

Serverless platforms typically adopt an early-binding approach for function sizing, requiring developers to specify an immutable size for each function within a workflow beforehand. Accounting for potential runtime variability, developers must size functions for worst-case scenarios to ensure service-level objectives (SLOs), resulting in significant resource inefficiency. To address this issue, we propose Janus, a novel resource adaptation framework for serverless platforms. Janus employs a late-binding approach, allowing function sizes to be dynamically adapted based on runtime conditions. The main challenge lies in the information barrier between the developer and the provider: developers lack access to runtime information, while providers lack domain knowledge about the workflow. To bridge this gap, Janus allows developers to provide hints containing rules and options for resource adaptation. Providers then follow these hints to dynamically adjust resource allocation at runtime based on real-time function execution information, ensuring compliance with SLOs. We implement Janus and conduct extensive experiments with real-world serverless workflows. Our results demonstrate that Janus enhances resource efficiency by up to 34.7% compared to the state-of-the-art.

cs.DC↗

arXiv2025

An Interpretable and Crosslingual Method for Evaluating Second-Language Dialogues

We analyse the cross-lingual transferability of a dialogue evaluation framework that assesses the relationships between micro-level linguistic features (e.g. backchannels) and macro-level interactivity labels (e.g. topic management), originally designed for English-as-a-second-language dialogues. To this end, we develop CNIMA (Chinese Non-Native Interactivity Measurement and Automation), a Chinese-as-a-second-language labelled dataset with 10K dialogues. We found the evaluation framework to be robust across distinct languages: English and Chinese, revealing language-specific and language-universal relationships between micro-level and macro-level features. Next, we propose an automated, interpretable approach with low data requirement that scores the overall quality of a second-language dialogue based on the framework. Our approach is interpretable in that it reveals the key linguistic and interactivity features that contributed to the overall quality score. As our approach does not require labelled data, it can also be adapted to other languages for second-language dialogue evaluation.

cs.CL↗

arXiv2025

Theoretical lower limit of coercive field in ferroelectric hafnia

The high coercive field ($E_c$) of hafnia-based ferroelectrics presents a major obstacle to their applications. The ferroelectric switching mechanisms in hafnia that dictate $E_c$, especially those related to domain nucleation in the Nucleation-Limited-Switching (NLS) model and domain wall motion in the Kolmogorov-Avrami-Ishibas (KAI) model, have remained elusive. We develop a deep-learning-assisted multiscale approach, incorporating atomistic insights into the critical nucleus, to predict both NLS- and KAI-type coercive fields. The theoretical NLS-type $E_c$ values agree with previous experimental results as well as our own measurements and also exhibit the correct thickness scaling for films between 3 and 20 nm. Combined theoretical and experimental investigations reveal that the giant $E_c$ in hafnia-based ferroelectrics arises from the ultra-thin geometry, which confines switching to the NLS mechanism. We predict that the theoretical lower limit for KAI-type $E_c$ is 0.1 MV/cm arsing from mobile domain walls. The activation of KAI-type switching to achieve lower $E_c$ is supported by our experimental demonstration of a low coercive field of 1 MV/cm in a 60 nm ferroelectric (HfO$_2$)$_n$/(ZrO$_2$)$_n$ ($n=3$ unit cells) superlattices. These findings establish a comprehensive framework for understanding ferroelectric switching in hafnia and highlight the potential of geometry and domain-wall engineering to achieve low-$E_c$ devices.

cond-mat.mtrl-sci↗

arXiv2025

Triply heavy tetraquark states in a mass-splitting model

In a modified chromomagnetic interaction model, assuming $X(4140)$ to be the lowest $1^{++}$ $cs\bar{c}\bar{s}$ tetraquark and treating it as the reference state, we systematically investigated the masses of the triply-heavy tetraquark states $QQ\bar{Q}\bar{q}$ ($Q=c,b;q=u,d,s$). Because of their higher masses, no stable tetraquarks were found. Using a simple scheme, we also estimated the partial widths of the rearrangement decay channels and relevant ratios. A compact triply heavy tetraquark candidate would be favored if its observed mass and partial width ratios were comparable with our predictions. We hope that the present work will be helpful for further studies.

hep-ph↗

arXiv2025

Small polarons mediated near-room-temperature metal-insulator transition in vanadium dioxide and their hopping dynamics

Researchers pursuing advanced photoelectric devices have discovered near room-temperature metal-insulator transitions (MIT) in non-volatile VO2. Despite theoretical investigations suggesting that polaron dynamics mediate the MIT, direct experimental evidence remains scarce. In this study, we present direct evidence of the polaron state in insulating VO2 through high-resolution spectroscopic ellipsometry measurements and first-principles calculations. We illustrate the complementary role of polaron dynamics in facilitating Peierls and Mott transitions, thereby contributing to the MIT processes. Furthermore, our observations and characterizations of conventional metallic and correlated plasmons in the respective phases of the VO2 film offer valuable insights into their electron structures. This investigation enhances comprehension of the MIT mechanism in correlated systems and underscores the roles of polarons, lattice distortions, and electron correlations in facilitating phase transition processes in strongly-correlated systems. Additionally, the detailed detection of small polarons and plasmons serves as inspiration for the development of new device functionalities.

physics.app-ph↗

arXiv2024

RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers

The high performance of tree ensemble classifiers benefits from a large set of rules, which, in turn, makes the models hard to understand. To improve interpretability, existing methods extract a subset of rules for approximation using model reduction techniques. However, by focusing on the reduced rule set, these methods often lose fidelity and ignore anomalous rules that, despite their infrequency, play crucial roles in real-world applications. This paper introduces a scalable visual analysis method to explain tree ensemble classifiers that contain tens of thousands of rules. The key idea is to address the issue of losing fidelity by adaptively organizing the rules as a hierarchy rather than reducing them. To ensure the inclusion of anomalous rules, we develop an anomaly-biased model reduction method to prioritize these rules at each hierarchical level. Synergized with this hierarchical organization of rules, we develop a matrix-based hierarchical visualization to support exploration at different levels of detail. Our quantitative experiments and case studies demonstrate how our method fosters a deeper understanding of both common and anomalous rules, thereby enhancing interpretability without sacrificing comprehensiveness.

cs.LG↗

arXiv2024

Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities

Purpose: To evaluate various Segmental Anything Model (SAM) prompt strategies across four lesions datasets and to subsequently develop a reinforcement learning (RL) agent to optimize SAM prompt placement. Materials and Methods: This retrospective study included patients with four independent ovarian, lung, renal, and breast tumor datasets. Manual segmentation and SAM-assisted segmentation were performed for all lesions. A RL model was developed to predict and select SAM points to maximize segmentation performance. Statistical analysis of segmentation was conducted using pairwise t-tests. Results: Results show that increasing the number of prompt points significantly improves segmentation accuracy, with Dice coefficients rising from 0.272 for a single point to 0.806 for five or more points in ovarian tumors. The prompt location also influenced performance, with surface and union-based prompts outperforming center-based prompts, achieving mean Dice coefficients of 0.604 and 0.724 for ovarian and breast tumors, respectively. The RL agent achieved a peak Dice coefficient of 0.595 for ovarian tumors, outperforming random and alternative RL strategies. Additionally, it significantly reduced segmentation time, achieving a nearly 10-fold improvement compared to manual methods using SAM. Conclusion: While increased SAM prompts and non-centered prompts generally improved segmentation accuracy, each pathology and modality has specific optimal thresholds and placement strategies. Our RL agent achieved superior performance compared to other agents while achieving a significant reduction in segmentation time.

eess.IV↗

arXiv2024

Doubly heavy tetraquark states in a mass splitting model

Treating the $X(4140)$ as a compact $J^{PC}=1^{++}$ $cs\bar{c}\bar{s}$ state and using its mass as a reference scale, we systematically estimate the masses of doubly heavy tetraquark states $QQ\bar{q}\bar{q}$ where $Q=c,b$ and $q=u,d,s$. Their decay properties are studied with a simple rearrangement scheme. Based on our results, the lowest $I(J^P)=0(1^+)$ $bb\bar{n}\bar{n}$ state is a stable tetraquark about 20 MeV below the $\bar{B}^*\bar{B}$ threshold. The mass and width of the low-mass $0(1^+)$ $cc\bar{n}\bar{n}$ ($n=u,d$) tetraquark are compatible with the $T_{cc}(3875)^+$ observed by the LHCb Collaboration. The location of the lowest $0(0^+)$ and $0(1^+)$ $bc\bar{n}\bar{n}$ states are found to be close to the $\bar{B}D$ and $\bar{B}^*D$ thresholds, respectively. We hope that the predicted ratios between partial widths of different channels may be helpful to identify compact tetraquark states from future measurements.

hep-ph↗

arXiv2024

Erasing Undesirable Influence in Diffusion Models

Diffusion models are highly effective at generating high-quality images but pose risks, such as the unintentional generation of NSFW (not safe for work) content. Although various techniques have been proposed to mitigate unwanted influences in diffusion models while preserving overall performance, achieving a balance between these goals remains challenging. In this work, we introduce EraseDiff, an algorithm designed to preserve the utility of the diffusion model on retained data while removing the unwanted information associated with the data to be forgotten. Our approach formulates this task as a constrained optimization problem using the value function, resulting in a natural first-order algorithm for solving the optimization problem. By altering the generative process to deviate away from the ground-truth denoising trajectory, we update parameters for preservation while controlling constraint reduction to ensure effective erasure, striking an optimal trade-off. Extensive experiments and thorough comparisons with state-of-the-art algorithms demonstrate that EraseDiff effectively preserves the model's utility, efficacy, and efficiency.

cs.CV↗

arXiv2024

TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data

Representation learning is a fundamental aspect of modern artificial intelligence, driving substantial improvements across diverse applications. While selfsupervised contrastive learning has led to significant advancements in fields like computer vision and natural language processing, its adaptation to tabular data presents unique challenges. Traditional approaches often prioritize optimizing model architecture and loss functions but may overlook the crucial task of constructing meaningful positive and negative sample pairs from various perspectives like feature interactions, instance-level patterns and batch-specific contexts. To address these challenges, we introduce TabDeco, a novel method that leverages attention-based encoding strategies across both rows and columns and employs contrastive learning framework to effectively disentangle feature representations at multiple levels, including features, instances and data batches. With the innovative feature decoupling hierarchies, TabDeco consistently surpasses existing deep learning methods and leading gradient boosting algorithms, including XG-Boost, CatBoost, and LightGBM, across various benchmark tasks, underscoring its effectiveness in advancing tabular data representation learning.

cs.LG↗