arXiv subjects

Hua Zhong

Publications and source records attributed to Hua Zhong.

At least 19 recordsLinked to original sources

arXiv2026

Vortex solitons in disclination quasicrystals

Being structures characterized by discrete rotational symmetry $\mathcal{C}_ν$ of order $ν$, photonic quasicrystals are capable to support stable propagation of linear vortex-carrying light beams and vortex solitons. However, the impact of discrete rotational symmetry $ν$ of quasicrystals on the properties of vortex light states was not investigated so far, as only the systems constructed using the simplest Penrose tiling or corresponding optically induced Penrose lattices were considered in this context. Here we propose a broad class of quasicrystals with global topological defects -- disclinations -- introduced into their structure that allows to produce new quasicrystalline structures with any desired order of discrete rotational symmetry from basic Penrose structure. Such global topological deformation substantially enriches linear spectrum of quasicrystals, allowing them to support new types of linear vortex states and bifurcating from them families of stable thresholdless vortex solitons with unusual intensity and phase distributions. We found two different classes of stable vortex solitons consisting of in-phase or out-of-phase pairs of closely located bright spots, with total intensity distribution reflecting particular discrete rotational symmetry of the quasicrystal with disclination. Remarkably, even low-charge vortex solitons can be stable in quasicrystals with disclinations, while stability intervals for them broaden with decrease of the discrete rotational symmetry $\mathcal{C}_ν$ of quasicrystal. Our results expand the theory of localization in quasicrystals to structures with global topological deformation, highlighting new prospects for robust transmission of power or information arising in these systems.

physics.optics↗

arXiv2026

Simultaneous Secrecy and Covert Communications (SSACC) in Mobility-Aware RIS-Aided Networks

In this paper, we propose a simultaneous secrecy and covert communications (SSACC) scheme in a reconfigurable intelligent surface (RIS)-aided network with a cooperative jammer. The scheme enhances communication security by maximizing the secrecy capacity and the detection error probability (DEP). Under a worst-case scenario for covert communications, we consider that the eavesdropper can optimally adjust the detection threshold to minimize the DEP. Accordingly, we derive closedform expressions for both average minimum DEP (AMDEP) and average secrecy capacity (ASC). To balance AMDEP and ASC, we propose a new performance metric and design an algorithm based on generative diffusion models (GDM) and deep reinforcement learning (DRL). The algorithm maximizes data rates under user mobility while ensuring high AMDEP and ASC by optimizing power allocation. Simulation results demonstrate that the proposed algorithm achieves faster convergence and superior performance compared to conventional deep deterministic policy gradient (DDPG) methods, thereby validating its effectiveness in balancing security and capacity performance.

eess.SP↗

arXiv2025

Well-posedness of the 3-D compressible Navier-Stokes equations with density-dependent viscosities in exterior domains with far-field vacuum

This paper investigates the local existence and uniqueness of strong solutions to the three-dimensional compressible Navier-Stokes equations with density-dependent viscosities in exterior domains. When both the shear and bulk viscosity coefficients depend on the density in a power law ($ρ^δ$ with $0<δ<1$) and Navier-slip boundary condition on the velocity is imposed, base on a reformulation of the problem using new variables to handle the degeneracy near vacuum, we establish the local well-posedness of regular solutions with far-field vacuum in inhomogeneous Sobolev spaces. Compared to the Cauchy problem, the initial-boundary value problem requires establishing non-standard weighted estimates and handling the unavailability of boundary conditions for higher-order terms. Our approach addresses these challenges via the conormal space technique.

math.AP↗

arXiv2025

A High-Capacity and Secure Disambiguation Algorithm for Neural Linguistic Steganography

Neural linguistic steganography aims to embed information into natural text while preserving statistical undetectability. A fundamental challenge in this ffeld stems from tokenization ambiguity in modern tokenizers, which can lead to catastrophic decoding failures. The recent method, SyncPool, addresses this ambiguity by employing a coarse-grained synchronization mechanism over groups of ambiguous candidates. However, SyncPool sacriffces embedding capacity, as it utilizes the entire Shannon entropy of an ambiguous group solely for synchronization rather than for payload embedding. We propose a method named look-ahead Sync, which overcomes the capacity limitation of SyncPool while retaining its provable security guarantees. Our approach performs minimal synchronized sampling only on truly indistinguishable token sequences, while strategically preserving all other discernible paths to maximize embedding capacity. We provide theoretical proofs for the security of our method and analyze the gap between its achievable embedding capacity and the theoretical upper bound. Experiments on English (using Llama 3) and Chinese (using Qwen 2.5) benchmarks show that our method consistently approaches the theoretical capacity upper bound and signiffcantly outperforms SyncPool. The improvement in embedding rate exceeds 160% in English and 25% in Chinese, particularly in settings with larger candidate pools. This work represents a signiffcant step toward practical high-capacity provably secure linguistic steganography.

cs.CL↗

arXiv2025

APRIL: API Synthesis with Automatic Prompt Optimization and Reinforcement Learning

APIs are central to modern software development, yet composing new APIs from large libraries is difficult due to the exponential search space; traditional component-based synthesis relies on costly exploration and hand-crafted specifications. While large language models (LLMs) can generate implementations from natural language, hallucinations and limited access to up-to-date contextual information often yield incorrect code. In this paper, we present APRIL, an approach that combines LLM-based synthesis with Automatic Prompt Optimization (APO) and Reinforcement Learning from Verifiable Rewards (RLVR): APO iteratively refines prompts for a frozen model, while RLVR fine-tunes the policy toward functional correctness, producing an efficient synthesis pipeline. Evaluated on 81 real-world APIs from widely used scientific Python libraries and benchmarked against instruction-tuned but unfine-tuned LLMs guided by expert prompts, APRIL achieves substantial improvements. These results indicate that integrating APO and RLVR provides a robust, scalable path for component-based API synthesis in large libraries.

cs.SE↗

arXiv2025

Strong solutions to the 3-D compressible MHD equations with density-dependent viscosities in exterior domains with far-field vacuum

This paper investigates the existence and uniqueness of local strong solutions to the three-dimensional compressible magnetohydrodynamic (MHD) equations with density-dependent viscosities in an exterior domain. The system models the dynamics of electrically conducting fluids, such as plasmas, and incorporates the effects of magnetic fields on fluid motion. We focus on the case where the viscosity coefficients are proportional to the fluid density, and the far-field density approaches vacuum. By introducing a reformulation of the problem using new variables to handle the degeneracy near vacuum, we establish the local well-posedness of strong solutions for arbitrarily large initial data, even in the presence of far-field vacuum. Our analysis employs energy estimates, elliptic regularity theory, and a careful treatment of the Navier-slip boundary conditions for the velocity and perfect conductivity conditions for the magnetic field. To the best of our knowledge, such results are not available even for the Cauchy problem to the 3-D compressible MHD equations with degenerate viscosities.

math.AP↗

arXiv2025

An approach for API synthesis using large language models

APIs play a pivotal role in modern software development by enabling seamless communication and integration between various systems, applications, and services. Component-based API synthesis is a form of program synthesis that constructs an API by assembling predefined components from a library. Existing API synthesis techniques typically implement dedicated search strategies over bounded spaces of possible implementations, which can be very large and time consuming to explore. In this paper, we present a novel approach of using large language models (LLMs) in API synthesis. LLMs offer a foundational technology to capture developer insights and provide an ideal framework for enabling more effective API synthesis. We perform an experimental evaluation of our approach using 135 real-world programming tasks, and compare it with FrAngel, a state-of-the-art API synthesis tool. The experimental results show that our approach completes 133 of the tasks, and overall outperforms FrAngel. We believe LLMs provide a very useful foundation for tackling the problem of API synthesis, in particular, and program synthesis, in general.

cs.SE↗

arXiv2025

MorphoITH: A Framework for Deconvolving Intra-Tumor Heterogeneity Using Tissue Morphology

The ability of tumors to evolve and adapt by developing subclones in different genetic and epigenetic states is a major challenge in oncology. Traditional tools like multi-regional sequencing used to study tumor evolution and the resultant intra-tumor heterogeneity (ITH) are often impractical because of their resource-intensiveness and limited scalability. Here, we present MorphoITH, a novel framework that leverages histopathology slides to deconvolve molecular ITH through tissue morphology. MorphoITH integrates a self-supervised deep learning similarity measure to capture phenotypic variation across multiple dimensions (cytology, architecture, and microenvironment) with rigorous methods to eliminate spurious sources of variation. Using a prototype of ITH, clear cell renal cell carcinoma (ccRCC), we show that MorphoITH captures clinically-significant biological features, such as vascular architecture and nuclear grades. Furthermore, we find that MorphoITH recognizes differential biological states corresponding to subclonal changes in key driver genes (BAP1/PBRM1/SETD2). Finally, by applying MorphoITH to a multi-regional sequencing experiment, we postulate evolutionary trajectories that largely recapitulate genetic evolution. In summary, MorphoITH provides a scalable phenotypic lens that bridges the gap between histopathology and genomics, advancing precision oncology.

q-bio.QM↗

arXiv2024

SOWAHA as a Cancer Suppressor Gene Influence Metabolic Reprogramming

SOWAHA is a protein-coding gene, also known as ANKRD43. Studies have indicated that SOWAHA can serve as a prognostic biomarker in colorectal cancer and pancreatic cancer. However, there are few reports about SOWAHA in other types of cancer and the specific mechanism of action of SOWAHA in cancer is also not clear. Based on National Center for Biotechnology Information (NCBI), The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), cBioPortal, Human Protein Atlas (HPA), etc., we adopted bioinformatics methods to uncover the potential tumor genomic features of SOWAHA, including the correlation with prognosis, gene mutation, immune cell infiltration, and DNA methylation in different tumors and evaluated the association with tumor heterogeneity, stemness, chemokines chemokine receptors, and immunomodulators in pan-cancer. Besides, we knocked down SOWAHA in SW620 cells and performed RNA-seq analysis, then we conducted functional enrichment to uncover the biological significance of the gene set. SOWAHA has early diagnostic potential, and low expression of SOWAHA was associated with poor prognosis in was associated with poor prognosis in GBMLGG, PAAD, READ, etc. SOWAHA is associated with most tumor immune-infiltrating cells in pan-cancer. SOWAHA correlates with DNA methylation, tumor heterogeneity, and stemness in many epithelial carcinomas. Furthermore, SOWAHA is involved in many enzyme activity and metabolic pathways, mainly metabolic programming pathways in cancer. Additionally, we identified two potential transcription factors of SOWAHA, TBX4, and FOXP2, which are dysregulated in SW620 cells. Besides, the cell proliferation and viability in siSOWAHA groups are better than in siNC groups.SOWAHA, identified as a suppressor gene, and its role in the progression of colorectal cancer is primarily mediated through metabolic reprogramming mechanisms.

q-bio.QM↗

arXiv2024

$\mathcal{PT}$-symmetric photonic lattices with type-II Dirac cones

The type-II Dirac cone is a special feature of the band structure, whose Fermi level is represented by a pair of crossing lines. It has been demonstrated that such a structure is useful for investigating topological edge solitons, and more specifically, for mimicking the Kline tunneling. However, it is still not clear what the interplay between type-II Dirac cones and the non-Hermiticity mechanism will result in. Here, this question is addressed; in particular, we report the $\mathcal{PT}$-symmetric photonic lattices with type-II Dirac cones for the first time. We identify a slope-exceptional ring and name it the type-II exceptional ring. We display the restoration of the $\mathcal{PT}$ symmetry of the lattice by reducing the separation between the sites in the unit cell. Curiously, the amplitude of the beam during propagation in the non-Hermitian lattice with $\mathcal{PT}$ symmetry only decays because of diffraction, whereas in the $\mathcal{PT}$ symmetry-broken lattice it will be amplified, even though the beam still diffracts. This work establishes the link between the non-Hermiticity mechanism and the violation of Lorentz invariance in these physical systems.

physics.optics↗

arXiv2024

Topological edge states in photonic Floquet insulator with unpaired Dirac cones

Topological insulators are most frequently constructed using lattices with specific degeneracies in their linear spectra, such as Dirac points. For a broad class of lattices, such as honeycomb ones, these points and associated Dirac cones generally appear in non-equivalent pairs. Simultaneous breakup of the time-reversal and inversion symmetry in systems based on such lattices may result in the formation of the unpaired Dirac cones in bulk spectrum, but the existence of topologically protected edge states in such structures remains an open problem. Here photonic Floquet insulator on honeycomb lattice with unpaired Dirac cones in its spectrum is introduced that can support unidirectional edge states appearing at the edge between two regions with opposite sublattice detuning. Topological properties of this system are characterized by the nonzero valley Chern number. Remarkably, edge states in this system can circumvent sharp corners without inter-valley scattering even though there is no total forbidden gap in the spectrum. Our results reveal unusual interplay between two different physical mechanisms of creation of topological edge states based on simultaneous breakup of different symmetries of the system.

physics.optics↗

arXiv2024

A Flexible Recursive Network for Video Stereo Matching Based on Residual Estimation

Due to the high similarity of disparity between consecutive frames in video sequences, the area where disparity changes is defined as the residual map, which can be calculated. Based on this, we propose RecSM, a network based on residual estimation with a flexible recursive structure for video stereo matching. The RecSM network accelerates stereo matching using a Multi-scale Residual Estimation Module (MREM), which employs the temporal context as a reference and rapidly calculates the disparity for the current frame by computing only the residual values between the current and previous frames. To further reduce the error of estimated disparities, we use the Disparity Optimization Module (DOM) and Temporal Attention Module (TAM) to enforce constraints between each module, and together with MREM, form a flexible Stackable Computation Structure (SCS), which allows for the design of different numbers of SCS based on practical scenarios. Experimental results demonstrate that with a stack count of 3, RecSM achieves a 4x speed improvement compared to ACVNet, running at 0.054 seconds based on one NVIDIA RTX 2080TI GPU, with an accuracy decrease of only 0.7%. Code is available at https://github.com/Y0uchenZ/RecSM.

cs.CV↗

arXiv2024

Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical Trial

Predictive biomarkers of treatment response are lacking for metastatic clear cell renal cell carcinoma (ccRCC), a tumor type that is treated with angiogenesis inhibitors, immune checkpoint inhibitors, mTOR inhibitors and a HIF2 inhibitor. The Angioscore, an RNA-based quantification of angiogenesis, is arguably the best candidate to predict anti-angiogenic (AA) response. However, the clinical adoption of transcriptomic assays faces several challenges including standardization, time delay, and high cost. Further, ccRCC tumors are highly heterogenous, and sampling multiple areas for sequencing is impractical. Here we present a novel deep learning (DL) approach to predict the Angioscore from ubiquitous histopathology slides. To overcome the lack of interpretability, one of the biggest limitations of typical DL models, our model produces a visual vascular network which is the basis of the model's prediction. To test its reliability, we applied this model to multiple cohorts including a clinical trial dataset. Our model accurately predicts the RNA-based Angioscore on multiple independent cohorts (spearman correlations of 0.77 and 0.73). Further, the predictions help unravel meaningful biology such as association of angiogenesis with grade, stage, and driver mutation status. Finally, we find our model can predict response to AA therapy, in both a real-world cohort and the IMmotion150 clinical trial. The predictive power of our model vastly exceeds that of CD31, a marker of vasculature, and nearly rivals the performance (c-index 0.66 vs 0.67) of the ground truth RNA-based Angioscore at a fraction of the cost. By providing a robust yet interpretable prediction of the Angioscore from histopathology slides alone, our approach offers insights into angiogenesis biology and AA treatment response.

q-bio.QM↗

arXiv2024

Observation of nonlinear fractal higher-order topological insulator

Higher-order topological insulators (HOTIs) are unique materials hosting topologically protected states, whose dimensionality is at least by a factor of 2 lower than that of the bulk. Topological states in such insulators may be strongly confined in their corners that leads to considerable enhancement of nonlinear processes involving such states. However, all nonlinear HOTIs demonstrated so far were built on periodic bulk lattice materials. Here we demonstrate first \textit{nonlinear photonic} HOTI with the fractal origin. Despite their fractional effective dimensionality, the HOTIs constructed here on two different types of the Sierpiński gasket waveguide arrays, may support topological corner states for unexpectedly wide range of coupling strengths, even in parameter regions where conventional HOTIs become trivial. We demonstrate thresholdless solitons bifurcating from corner states in nonlinear fractal HOTIs and show that their localization can be efficiently controlled by the input beam power. We observe sharp differences in nonlinear light localization on outer and multiple inner corners and edges representative for these fractal materials. Our findings not only represent a new paradigm for nonlinear topological insulators, but also open new avenues for potential applications of fractal materials to control the light flow.

physics.optics↗

arXiv2022

PAANet:Visual Perception based Four-stage Framework for Salient Object Detection using High-order Contrast Operator

It is believed that human vision system (HVS) consists of pre-attentive process and attention process when performing salient object detection (SOD). Based on this fact, we propose a four-stage framework for SOD, in which the first two stages match the \textbf{P}re-\textbf{A}ttentive process consisting of general feature extraction (GFE) and feature preprocessing (FP), and the last two stages are corresponding to \textbf{A}ttention process containing saliency feature extraction (SFE) and the feature aggregation (FA), namely \textbf{PAANet}. According to the pre-attentive process, the GFE stage applies the fully-trained backbone and needs no further finetuning for different datasets. This modification can greatly increase the training speed. The FP stage plays the role of finetuning but works more efficiently because of its simpler structure and fewer parameters. Moreover, in SFE stage we design for saliency feature extraction a novel contrast operator, which works more semantically in contrast with the traditional convolution operator when extracting the interactive information between the foreground and its surroundings. Interestingly, this contrast operator can be cascaded to form a deeper structure and extract higher-order saliency more effective for complex scene. Comparative experiments with the state-of-the-art methods on 5 datasets demonstrate the effectiveness of our framework.

cs.CV↗

arXiv2021

Global Solutions to an initial boundary problem for the compressible 3-D MHD equations with Navier-slip and perfectly conducting boundary conditions in exterior domains

An initial boundary value problem for compressible Magnetohydrodynamics (MHD) is considered on an exterior domain (with the first Betti number vanishes) in $R^3$ in this paper. The global existence of smooth solutions near a given constant state for compressible MHD with the boundary conditions of Navier-slip for the velocity filed and perfect conduction for the magnetic field is established. Moreover the explicit decay rate is given. In particular, the results obtained in this paper also imply the global existence of classical solutions for the full compressible Navier-Stokes equations with Navier-slip boundary conditions on exterior domains in three dimensions, which is not available in literature, to the best of knowledge of the authors'.

math.AP↗

arXiv2021

Theory of Topological Corner State Laser in Kagome Waveguide Arrays

In comparison with conventional lasers, topological lasers are more robust and can be immune to disorder or de-fects if lasing occurs in topologically protected states. Previously reported topological lasers were almost exclu-sively based on the first-order photonic topological insulators. Here, we show that lasing can be achieved in the zero-dimensional corner state in a second-order photonic topological insulator, which is based on Kagome wave-guide array with a rhombic configuration. If gain is present in the corner of the structure, where topological corner state resides, stable lasing in this state is achieved, with lowest possible threshold, in the presence of uniform loss-es and two-photon absorption. When gain acts in other corners of the structure, lasing may occur in edge or bulk states, but it requires substantially larger thresholds and transition to stable lasing occurs over much larger propa-gation distances, sometimes due to instabilities, which are absent for lasing in corner states. We find that increasing two-photon absorption generally plays strong stabilizing action for nonlinear lasing states. The transition to stable lasing stimulated by noisy inputs is illustrated. Our work demonstrates the realistic setting for corner state laser based on higher-order topological insulator realised with waveguide arrays.

physics.optics↗

arXiv2020

Nonlinear Topological Valley Hall Edge States Arising from Type-II Dirac Cones

Type-II Dirac/Weyl points, although impermissible in particle physics due to Lorentz covariance, were uncovered in condensed matter physics, driven by fundamental interest and intriguing applications of topological materials. Recently, there has been a surge of exploration of such generic points using various engineered platforms including photonic crystals, waveguide arrays, metasurfaces, magnetized plasma and polariton micropillars, aiming towards relativistic quantum emulation and understanding of exotic topological phenomena. Such endeavors, however, have focused mainly on linear topological states in real or synthetic Dirac/Weyl materials. Here, we demonstrate nonlinear valley Hall edge states (VHESs) in laser-writing anisotropic photonic lattices hosting type-II Dirac points. These self-trapped VHESs, manifested as topological gap quasi-solitons, are fundamentally distinct from their linear counterparts in type-I lattices and from all previously found topological solitons. Our finding may provide a route for understanding nonlinear phenomena in Lorentz-violating topological systems and for developing advanced light sources from topological insulator lasers.

physics.optics↗