arXiv subjects

Yong Zhou

Publications and source records attributed to Yong Zhou.

At least 37 records · Page 2Linked to original sources

arXiv2025

Muckenhoupt-weighted $L_q(L_p)$ boundedness for time-space fractional nonlocal operators

We develop a weighted mixed-norm $L_q(L_p)$-estimates for solutions to fractional evolution equations of the form \[ \partial_t^αw(t,x) = ϕ(Δ) w(t,x) + h(t,x), \quad w(0,\cdot) = w_0, \quad t > 0, \; x \in \mathbb{R}^d, \] where $\partial_t^α$ denotes the Caputo derivative of $α\in (0,1)$ and $ϕ(Δ)$ is a nonlocal operator associated with a Bernstein function $ϕ$. For all $p, q \in (1, \infty)$ and $γ\in \mathbb{R}$, we prove the estimate \begin{align*} &\left\| \partial_t^αw \right\|_{L_q(0,T,μ_2dt; H^{ϕ,γ}_p(μ_1))} + \left\| ϕ(Δ) w \right\|_{L_q(0,T,μ_2dt; H^{ϕ,γ}_p(μ_1))} \\ &\qquad\leq C \left( \left\| h \right\|_{L_q(0,T,μ_2dt; H^{ϕ,γ}_p(μ_1))} + \left\| w_0 \right\|_{N_{α,p,ϕ}} \right), \end{align*} where $μ_1\in A_p(\mathbb{R}^d)$ and $μ_2\in A_q(\mathbb{R})$ are Muckenhoupt weights, and $N_{α,p,ϕ}$ is a Banach space characterizing admissible initial data. In particular, when $μ_2\equiv 1$ and $αq>1$, $N_{α,p,ϕ}$ coincides with the weighted Besov space $B^{ϕ,γ+2-\frac{2}{αq}}_{p,q}(μ_1)$. The analysis employs tools from harmonic analysis, including the Fefferman--Stein inequality, Hardy-Littlewood maximal estimates in weighted mixed-norm spaces, and sharp function methods for bounding solution operators. These results extend and unify previous work by K.~H.~Kim et al, providing a general analytic framework for weighted $L_q(L_p)$-theory of time-space nonlocal evolution equations.

math.AP↗

arXiv2025

Semi-supervised Image Dehazing via Expectation-Maximization and Bidirectional Brownian Bridge Diffusion Models

Existing dehazing methods deal with real-world haze images with difficulty, especially scenes with thick haze. One of the main reasons is the lack of real-world paired data and robust priors. To avoid the costly collection of paired hazy and clear images, we propose an efficient semi-supervised image dehazing method via Expectation-Maximization and Bidirectional Brownian Bridge Diffusion Models (EM-B3DM) with a two-stage learning scheme. In the first stage, we employ the EM algorithm to decouple the joint distribution of paired hazy and clear images into two conditional distributions, which are then modeled using a unified Brownian Bridge diffusion model to directly capture the structural and content-related correlations between hazy and clear images. In the second stage, we leverage the pre-trained model and large-scale unpaired hazy and clear images to further improve the performance of image dehazing. Additionally, we introduce a detail-enhanced Residual Difference Convolution block (RDC) to capture gradient-level information, significantly enhancing the model's representation capability. Extensive experiments demonstrate that our EM-B3DM achieves superior or at least comparable performance to state-of-the-art methods on both synthetic and real-world datasets.

cs.CV↗

arXiv2025

Cauchy problems for time-space fractional coupled chemotaxis-fluid equations in Besov-Morrey spaces

In this paper, we consider the Cauchy problems for the time-space fractional coupled chemotaxis-fluid equations, which is a generalized form of the coupled chemotaxis-fluid equations studied in \cite{M.H. Yang}. In contrast to \cite{M.H. Yang}, the solution operator of the system does not satisfy the semigroup effect, which makes the approach of \cite{M.H. Yang} inapplicable. Based on the theory of harmonic analysis, using techniques such as real interpolation, embedding in Besov-Morrey spaces, the multiplier theorem, and the Hardy-Littelwood inequality in Morrey spaces, we establish global existence. As an application, we analysis the asymptotic behavior of the solutions.

math.AP↗

arXiv2025

On the well-posedness of time-space fractional Schrödinger equation on $\mathbb{R}^{d}$

This paper considers the well-posedness of a class of time-space fractional Schrödinger equations introduced by Naber. In contrast to the classical Schrödinger equation, the solution operator here exhibits derivative loss and lacks the structure of a semigroup, which makes the classical Strichartz estimates inapplicable. By using harmonic analysis tools -- including the smoothing effect theory of Kenig and Ponce for Korteweg-de Vries equations \cite[\emph{Commun.~Pure Appl.~Math.}]{Kenig}, real interpolation techniques, and the Van der Corput lemma -- we establish novel dispersive estimates for the solution operator. These estimates generalize Ponce's regularity results \cite[\emph{J.~Funct.~Anal.}]{Ponce} for oscillatory integrals and enable us to address the derivative loss in the Schrödinger kernel. For the cases $β<2$~(in one space dimension) and $β>2$~(in higher dimensions), we prove local and global well-posedness in Sobolev and Lorentz-type spaces, respectively. Additionally, we analyze the asymptotic behavior of solutions and demonstrate the existence of self-similar solutions under homogeneous initial data. The results highlight the interplay between fractional derivatives, dispersive properties, and nonlinear dynamics, extending the understanding of nonlocal evolution equations in quantum mechanics and related fields.

math.AP↗

arXiv2025

MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution

Facial micro-expression recognition (MER) is a challenging problem, due to transient and subtle micro-expression (ME) actions. Most existing methods depend on hand-crafted features, key frames like onset, apex, and offset frames, or deep networks limited by small-scale and low-diversity datasets. In this paper, we propose an end-to-end micro-action-aware deep learning framework with advantages from transformer, graph convolution, and vanilla convolution. In particular, we propose a novel F5C block composed of fully-connected convolution and channel correspondence convolution to directly extract local-global features from a sequence of raw frames, without the prior knowledge of key frames. The transformer-style fully-connected convolution is proposed to extract local features while maintaining global receptive fields, and the graph-style channel correspondence convolution is introduced to model the correlations among feature patterns. Moreover, MER, optical flow estimation, and facial landmark detection are jointly trained by sharing the local-global features. The two latter tasks contribute to capturing facial subtle action information for MER, which can alleviate the impact of insufficient training data. Extensive experiments demonstrate that our framework (i) outperforms the state-of-the-art MER methods on CASME II, SAMM, and SMIC benchmarks, (ii) works well for optical flow estimation and facial landmark detection, and (iii) can capture facial subtle muscle actions in local regions associated with MEs. The code is available at https://github.com/CYF-cuber/MOL.

cs.CV↗

arXiv2025

Hierarchical Learning and Computing over Space-Ground Integrated Networks

Space-ground integrated networks hold great promise for providing global connectivity, particularly in remote areas where large amounts of valuable data are generated by Internet of Things (IoT) devices, but lacking terrestrial communication infrastructure. The massive data is conventionally transferred to the cloud server for centralized artificial intelligence (AI) models training, raising huge communication overhead and privacy concerns. To address this, we propose a hierarchical learning and computing framework, which leverages the lowlatency characteristic of low-earth-orbit (LEO) satellites and the global coverage of geostationary-earth-orbit (GEO) satellites, to provide global aggregation services for locally trained models on ground IoT devices. Due to the time-varying nature of satellite network topology and the energy constraints of LEO satellites, efficiently aggregating the received local models from ground devices on LEO satellites is highly challenging. By leveraging the predictability of inter-satellite connectivity, modeling the space network as a directed graph, we formulate a network energy minimization problem for model aggregation, which turns out to be a Directed Steiner Tree (DST) problem. We propose a topologyaware energy-efficient routing (TAEER) algorithm to solve the DST problem by finding a minimum spanning arborescence on a substitute directed graph. Extensive simulations under realworld space-ground integrated network settings demonstrate that the proposed TAEER algorithm significantly reduces energy consumption and outperforms benchmarks.

cs.LG↗

arXiv2025

Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking

To reduce the reliance on large-scale annotations, self-supervised RGB-T tracking approaches have garnered significant attention. However, the omission of the object region by erroneous pseudo-label or the introduction of background noise affects the efficiency of modality fusion, while pseudo-label noise triggered by similar object noise can further affect the tracking performance. In this paper, we propose GDSTrack, a novel approach that introduces dynamic graph fusion and temporal diffusion to address the above challenges in self-supervised RGB-T tracking. GDSTrack dynamically fuses the modalities of neighboring frames, treats them as distractor noise, and leverages the denoising capability of a generative model. Specifically, by constructing an adjacency matrix via an Adjacency Matrix Generator (AMG), the proposed Modality-guided Dynamic Graph Fusion (MDGF) module uses a dynamic adjacency matrix to guide graph attention, focusing on and fusing the object's coherent regions. Temporal Graph-Informed Diffusion (TGID) models MDGF features from neighboring frames as interference, and thus improving robustness against similar-object noise. Extensive experiments conducted on four public RGB-T tracking datasets demonstrate that GDSTrack outperforms the existing state-of-the-art methods. The source code is available at https://github.com/LiShenglana/GDSTrack.

cs.CV↗

arXiv2025

Edge Large AI Models: Revolutionizing 6G Networks

Large artificial intelligence models (LAMs) possess human-like abilities to solve a wide range of real-world problems, exemplifying the potential of experts in various domains and modalities. By leveraging the communication and computation capabilities of geographically dispersed edge devices, edge LAM emerges as an enabling technology to empower the delivery of various real-time intelligent services in 6G. Unlike traditional edge artificial intelligence (AI) that primarily supports a single task using small models, edge LAM is featured by the need of the decomposition and distributed deployment of large models, and the ability to support highly generalized and diverse tasks. However, due to limited communication, computation, and storage resources over wireless networks, the vast number of trainable neurons and the substantial communication overhead pose a formidable hurdle to the practical deployment of edge LAMs. In this paper, we investigate the opportunities and challenges of edge LAMs from the perspectives of model decomposition and resource management. Specifically, we propose collaborative fine-tuning and full-parameter training frameworks, alongside a microservice-assisted inference architecture, to enhance the deployment of edge LAM over wireless networks. Additionally, we investigate the application of edge LAM in air-interface designs, focusing on channel prediction and beamforming. These innovative frameworks and applications offer valuable insights and solutions for advancing 6G technology.

cs.NI↗

arXiv2025

Maximal $L_p$-regularity for fractional problem driven by non-autonomous forms

We investigate the maximal $L_p$-regularity in J.L. Lions' problem involving a time-fractional derivative and a non-autonomous form $a(t;\cdot,\cdot)$ on a Hilbert space $H$. This problem says whether the maximal $L_p$-regularity in $H$ hold when $t \mapsto a(t ; u, v)$ is merely continuous or even merely measurable. We prove the maximal $L_p$-regularity results when the coefficients satisfy general Dini-type continuity conditions. In particular, we construct a counterexample to negatively answer this problem, indicating the minimal Hölder-scale regularity required for positive results.

math.CA↗

arXiv2025

Structured IB: Improving Information Bottleneck with Structured Feature Learning

The Information Bottleneck (IB) principle has emerged as a promising approach for enhancing the generalization, robustness, and interpretability of deep neural networks, demonstrating efficacy across image segmentation, document clustering, and semantic communication. Among IB implementations, the IB Lagrangian method, employing Lagrangian multipliers, is widely adopted. While numerous methods for the optimizations of IB Lagrangian based on variational bounds and neural estimators are feasible, their performance is highly dependent on the quality of their design, which is inherently prone to errors. To address this limitation, we introduce Structured IB, a framework for investigating potential structured features. By incorporating auxiliary encoders to extract missing informative features, we generate more informative representations. Our experiments demonstrate superior prediction accuracy and task-relevant information preservation compared to the original IB Lagrangian method, even with reduced network size.

cs.IT↗

arXiv2024

A Survey on Integrated Sensing, Communication, and Computation

The forthcoming generation of wireless technology, 6G, aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligent. This vision requires the seamless integration of three fundamental modules: Sensing for information acquisition, communication for information sharing, and computation for information processing and decision-making. These modules are intricately linked, especially in complex tasks such as edge learning and inference. However, the performance of these modules is interdependent, creating a resource competition for time, energy, and bandwidth. Existing techniques like integrated communication and computation (ICC), integrated sensing and computation (ISC), and integrated sensing and communication (ISAC) have made partial strides in addressing this challenge, but they fall short of meeting the extreme performance requirements. To overcome these limitations, it is essential to develop new techniques that comprehensively integrate sensing, communication, and computation. This integrated approach, known as Integrated Sensing, Communication, and Computation (ISCC), offers a systematic perspective for enhancing task performance. This paper begins with a comprehensive survey of historic and related techniques such as ICC, ISC, and ISAC, highlighting their strengths and limitations. It then discusses the benefits, functions, and challenges of ISCC. Subsequently, the state-of-the-art signal designs for ISCC, along with network resource management strategies specifically tailored for ISCC are explored. Furthermore, this paper discusses the exciting research opportunities that lie ahead for implementing ISCC in future advanced networks, and the unresolved issues requiring further investigation. ISCC is expected to unlock the full potential of intelligent connectivity, paving the way for groundbreaking applications and services.

cs.IT↗

arXiv2024

RQFormer: Rotated Query Transformer for End-to-End Oriented Object Detection

Oriented object detection presents a challenging task due to the presence of object instances with multiple orientations, varying scales, and dense distributions. Recently, end-to-end detectors have made significant strides by employing attention mechanisms and refining a fixed number of queries through consecutive decoder layers. However, existing end-to-end oriented object detectors still face two primary challenges: 1) misalignment between positional queries and keys, leading to inconsistency between classification and localization; and 2) the presence of a large number of similar queries, which complicates one-to-one label assignments and optimization. To address these limitations, we propose an end-to-end oriented detector called the Rotated Query Transformer, which integrates two key technologies: Rotated RoI Attention (RRoI Attention) and Selective Distinct Queries (SDQ). First, RRoI Attention aligns positional queries and keys from oriented regions of interest through cross-attention. Second, SDQ collects queries from intermediate decoder layers and filters out similar ones to generate distinct queries, thereby facilitating the optimization of one-to-one label assignments. Finally, extensive experiments conducted on four remote sensing datasets and one scene text dataset demonstrate the effectiveness of our method. To further validate its generalization capability, we also extend our approach to horizontal object detection The code is available at \url{https://github.com/wokaikaixinxin/RQFormer}.

cs.CV↗

arXiv2024

Atomistic catalyst polarization stemming hydrogen generation from CH4

As the extremely-sized nanocrystals and nanopores, an adatom M and atomic vacancy V exhibit extraordinary capability of catalysis with however little knowledge about the catalyst-reactant interfacial bonding dynamics. With the aid of DFT calculations, we examined the dehydrogenization of a single CH4 molecule catalyzed using the Rh(111,100), W(110), Ru(0001) surfaces, and monolayer graphene, with and without M or V. It is uncovered in the following three components: (i) catalyst polarization due to atomic under- or hetero-coordination raises the valence band of the catalyst by bond contraction and atomistic dipolar MP and vacancy dipolar V formation; (ii) reactant bond elongation by the interplay of the MP-V = H attraction and MP-V = C repulsion with the = denoting the negative pole of the MP-V; and (iii) reactant conversion, i.e., the scale of H-C elongation, the catalyst valence-band shift, the adsorption energy, and the catalytic activity are proportional to the charge quantity of the MP-V whose local electric field matters.

cond-mat.mtrl-sci↗

arXiv2024

PandaX-xT: a Multi-ten-tonne Liquid Xenon Observatory at the China Jinping Underground Laboratory

We propose a major upgrade to the existing PandaX-4T experiment in the China Jinping Underground Laboratory. The new experiment, PandaX-xT, will be a multi-ten-tonne liquid xenon, ultra-low background, and general-purpose observatory. The full-scaled PandaX-xT contains a 43-tonne liquid xenon active target. Such an experiment will significantly advance our fundamental understanding of particle physics and astrophysics. The sensitivity of dark matter direct detection will be improved by nearly two orders of magnitude compared to the current best limits, approaching the so-called "neutrino floor" for a dark matter mass above 10 GeV/$c^2$, providing a decisive test to the Weakly Interacting Massive Particle paradigm. By searching for the neutrinoless double beta decay of $^{136}$Xe isotope in the detector, the effective Majorana neutrino mass can be measured to a [10 -- 41] meV/$c^2$ sensitivity, providing a key test to the Dirac/Majorana nature of neutrino s. Astrophysical neutrinos and other ultra-rare interactions can also be measured and searched for with an unprecedented background level, opening up new windows of discovery. Depending on the findings, PandaX-xT will seek the next stage upgrade utilizing isotopic separation on natural xenon.

hep-ex↗

arXiv2024

Learning from Reduced Labels for Long-Tailed Data

Long-tailed data is prevalent in real-world classification tasks and heavily relies on supervised information, which makes the annotation process exceptionally labor-intensive and time-consuming. Unfortunately, despite being a common approach to mitigate labeling costs, existing weakly supervised learning methods struggle to adequately preserve supervised information for tail samples, resulting in a decline in accuracy for the tail classes. To alleviate this problem, we introduce a novel weakly supervised labeling setting called Reduced Label. The proposed labeling setting not only avoids the decline of supervised information for the tail samples, but also decreases the labeling costs associated with long-tailed data. Additionally, we propose an straightforward and highly efficient unbiased framework with strong theoretical guarantees to learn from these Reduced Labels. Extensive experiments conducted on benchmark datasets including ImageNet validate the effectiveness of our approach, surpassing the performance of state-of-the-art weakly supervised methods.

cs.LG↗

arXiv2024

Improving Wheatstone Bridge Sensitivity with Computational Simulations and Bayesian Optimization

The Wheatstone bridge experiment is fundamental for precise measurement of electrical resistance, holding significant value in both undergraduate physics education and real-life scientific research. This study reimagines the experiment by integrating computational simulation with traditional methods, enhancing its educational and practical value. By analyzing key factors such as internal resistances of the galvanometer and power supply and optimizing resistor configurations, we demonstrate pathways to maximize sensitivity. A Bayesian optimization-based software tool was also developed to automate sensitivity calculations, guiding optimal component selection. This approach bridges theoretical concepts and experimental applications, equipping students with valuable skills in both experimental and computational aspects of physics and preparing students for modern scientific challenges.

physics.ed-ph↗

arXiv2024

Theoretical Analysis of Solvent Effect on NAPBr Dye's Two-photon Absorption Ability and Non-Radiative Transition in Lipid Droplets Detection

Two-photon fluorescence imaging has shown a promising application in biomedical imaging due to its outstanding advantages such as large penetration depth, low photo-damage, and photo-bleaching, etc. Among them, the two-photon fluorescent dye NAPBr, which can effectively select and monitor lipid droplets in living cells and biological tissues, has attracted extensive attention because of its excellent fluorescent properties. However, the research on the fluorescent abilities of two-photon fluorescent dyes in solvent environment is not sufficient. In our work, theoretical analysis reveals the internal mechanism of the solvent effect on geometric structure and photophysical properties of two-photon fluorescent dyes, especially non-radiative transition process, and holes-electrons distribution and transfer. This can provide a reference for the development of efficient two-photon absorption (TPA) molecules with aggregation-induced emission (AIE) characteristics. Related data also showed good regularity. Moreover, dye in four solvents have excellent photophysical properties: high fluorescence quantum efficiency (up to 66.60%), large Stokes shift (up to 108696 cm-1), and two-photon absorption cross section (up to 3658 GM). The medium dielectric constant solution environment can achieve a balance between two-photon absorption and fluorescence emission capabilities better, which lays a solid foundation for the study of TPA molecules with AIE functions in terms of solvent effects.

physics.chem-ph↗

arXiv2024

Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample

Facial action unit (AU) detection remains a challenging task, due to the subtlety, dynamics, and diversity of AUs. Recently, the prevailing techniques of self-attention and causal inference have been introduced to AU detection. However, most existing methods directly learn self-attention guided by AU detection, or employ common patterns for all AUs during causal intervention. The former often captures irrelevant information in a global range, and the latter ignores the specific causal characteristic of each AU. In this paper, we propose a novel AU detection framework called AC2D by adaptively constraining self-attention weight distribution and causally deconfounding the sample confounder. Specifically, we explore the mechanism of self-attention weight distribution, in which the self-attention weight distribution of each AU is regarded as spatial distribution and is adaptively learned under the constraint of location-predefined attention and the guidance of AU detection. Moreover, we propose a causal intervention module for each AU, in which the bias caused by training samples and the interference from irrelevant AUs are both suppressed. Extensive experiments show that our method achieves competitive performance compared to state-of-the-art AU detection approaches on challenging benchmarks, including BP4D, DISFA, GFT, and BP4D+ in constrained scenarios and Aff-Wild2 in unconstrained scenarios. The code is available at https://github.com/ZhiwenShao/AC2D.

cs.CV↗