arXiv subjects

Tao Zhou

Publications and source records attributed to Tao Zhou.

At least 127 records · Page 7Linked to original sources

arXiv2025

Text-driven Multiplanar Visual Interaction for Semi-supervised Medical Image Segmentation

Semi-supervised medical image segmentation is a crucial technique for alleviating the high cost of data annotation. When labeled data is limited, textual information can provide additional context to enhance visual semantic understanding. However, research exploring the use of textual data to enhance visual semantic embeddings in 3D medical imaging tasks remains scarce. In this paper, we propose a novel text-driven multiplanar visual interaction framework for semi-supervised medical image segmentation (termed Text-SemiSeg), which consists of three main modules: Text-enhanced Multiplanar Representation (TMR), Category-aware Semantic Alignment (CSA), and Dynamic Cognitive Augmentation (DCA). Specifically, TMR facilitates text-visual interaction through planar mapping, thereby enhancing the category awareness of visual features. CSA performs cross-modal semantic alignment between the text features with introduced learnable variables and the intermediate layer of visual features. DCA reduces the distribution discrepancy between labeled and unlabeled data through their interaction, thus improving the model's robustness. Finally, experiments on three public datasets demonstrate that our model effectively enhances visual features with textual information and outperforms other methods. Our code is available at https://github.com/taozh2017/Text-SemiSeg.

cs.CV↗

arXiv2025

Quasinormal modes of accelerating spacetime

We calculate the exact values of the quasinormal frequencies for massless perturbations with spin $s\leq2$ moving in pure accelerating spacetime. We use two different methods to transfer the perturbation equations into the form of hypergeometric differential equations and obtain the same quasinormal frequencies. These purely imaginary spectra are shown to be independent of the spin of the perturbation and match those of the so-called acceleration modes of accelerating black holes after taking the Minkowski limit. This implies that the acceleration modes actually originate from the pure accelerating spacetime and the appearance of black holes would deform the spectra. In addition, we calculate the quasinormal frequencies of scalar, electromagnetic and gravitational perturbations of $D$-dimensional de Sitter spacetime and compare them with previous results to verify the validity of our method.

gr-qc↗

arXiv2025

Continual Retinal Vision-Language Pre-training upon Incremental Imaging Modalities

Traditional fundus image analysis models focus on single-modal tasks, ignoring fundus modality complementarity, which limits their versatility. Recently, retinal foundation models have emerged, but most still remain modality-specific. Integrating multiple fundus imaging modalities into a single foundation model is valuable. However, in dynamic environments, data from different modalities often arrive incrementally, necessitating continual pre-training. To address this, we propose RetCoP, the first continual vision-language pre-training framework in the fundus domain, which incrementally integrates image and text features from different imaging modalities into a single unified foundation model. To mitigate catastrophic forgetting in continual pre-training, we introduce a rehearsal strategy utilizing representative image-text pairs and an off-diagonal information distillation approach. The former allows the model to revisit knowledge from previous stages, while the latter explicitly preserves the alignment between image and text representations. Experiments show that RetCoP outperforms all the compared methods, achieving the best generalization and lowest forgetting rate. The code can be found at https://github.com/Yuang-Yao/RetCoP.

cs.CV↗

arXiv2025

Probing Sign-Changing Order Parameters via Impurity States in unconventional superconductors: Implications for La$_3$Ni$_2$O$_7$ Superconductors with interlayer pairing

Motivated by the desire to investigate the fundamental relationship between impurity-induced states and the sign change of the superconducting order parameter, as well as to explore the impurity effects in Ruddlesden-Popper nickelate superconductors with interlayer pairing, we employ the $T$-matrix approach to study single impurity scattering in unconventional superconductors. Our work focuses on two distinct pairing scenarios: intralayer $d$-wave pairing and interlayer $s$-wave pairing. For systems with intralayer $d$-wave pairing, we establish an intrinsic connection between the $d$-wave pairing symmetry and the emergence of mid-gap resonant states. Through a combination of analytical derivations and numerical simulations, we demonstrate that the appearance of in-gap states is directly linked to the sign reversal of the order parameter along the Fermi surface. In interlayer pairing systems, our results reveal the presence of pronounced resonant peaks, which can also be attributed to the sign-changing nature of the order parameter. We further extend our analysis to the bilayer nickelate superconductor La$_3$Ni$_2$O$_7$, providing a theoretical investigative of impurity effects in this material. Our findings not only elucidate the complex interplay between pairing symmetries and impurity-induced states in unconventional superconductors but also offer a powerful tool for probing the pairing mechanisms in nickelate-based high-temperature superconductors. This work lays the groundwork for future experimental and theoretical investigations into the unique electronic properties of these emerging materials.

cond-mat.supr-con↗

arXiv2025

Identifying discreditable firms in a large-scale ownership network

Violations of laws and regulations about food safety, production safety, quality standard and environmental protection, or negative consequences from loan, guarantee and pledge contracts, may result in operating and credit risks of firms. The above illegal or trust-breaking activities are collectively called discreditable activities, and firms with discreditable activities are named as discreditable firms. Identification of discreditable firms is of great significance for investment attraction, bank lending, equity investment, supplier selection, job seeking, and so on. In this paper, we collect registration records of about 113 million Chinese firms and construct an ownership network with about 6 million nodes, where each node is a firm who has invested at least one firm or has been invested by at least one firm. Analysis of publicly available records of discreditable activities show strong network effect, namely the probability of a firm to be discreditable is remarkably higher than the average probability given the fact that one of its investors or investees is discreditable. In comparison, for the risk of being a discreditable firm, an investee has higher impact than an investor in average. The impact of a firm on surrounding firms decays along with the increasing topological distance, analogous to the well-known "three degrees of separation" phenomenon. The uncovered correlation of discreditable activities can be considered as a representative example of network effect, in addition to the propagation of diseases, opinions and human behaviors. Lastly, we show that the utilization of the network effect largely improves the accuracy of the algorithm to identify discreditable firms.

cs.CY↗

arXiv2025

High coherence fluxonium manufactured with a wafer-scale uniformity process

Fluxonium qubits are recognized for their high coherence times and high operation fidelities, attributed to their unique design incorporating a superinductor, which is typically implemented using an array of over 100 Josephson junctions. However, this complexity poses significant fabrication challenges, particularly in achieving high yield and junction uniformity with traditional methods. Here, we introduce an overlap process for Josephson junction fabrication that achieves nearly 100% yield and maintains uniformity across a 2-inch wafer with less than 5% variation for the phase slip junction and less than 2% for the entire junction array. We use a compact junction array design that achieves state-of-the-art dielectric loss tangents and flux noise levels, as confirmed by multiple devices. This enables fluxonium qubits to reach energy relaxation times exceeding 1 millisecond at the flux frustration point. This work paves the way for scalable high coherence fluxonium processors using CMOS-compatible processes, marking a significant step towards practical quantum computing.

quant-ph↗

arXiv2025

Nonradial stability of self-similar blowup to Keller-Segel equation in three dimensions

In three dimensions, the parabolic-elliptic Keller-Segel system exhibits a rich variety of singularity formations. Notably, it admits an explicit self-similar blow-up solution whose radial stability, conjectured more than two decades ago in [Brenner-Constantin-Kadanoff-Schenkel-Venkataramani, 1999], was recently confirmed by [Glogić-Schörkhuber, 2024]. This paper aims to extend the radial stability to the nonradial setting, building on the finite-codimensional stability analysis in our previous work [Li-Zhou, 2024]. The main input is the mode stability of the linearized operator, whose nonlocal nature presents essential challenges for the spectral analysis. Besides a quantitative perturbative analysis for the high spherical classes, we adapt in the first spherical class the wave operator method of [Li-Wei-Zhang, 2020] for the fluid stability to localize the operator and remove the known unstable mode simultaneously. Our method provides localization beyond the partial mass variable and is independent of the explicit formula of the profile, so it potentially sheds light on other linear nonlocal problems.

math.AP↗

arXiv2025

Finite-time blowup for Keller-Segel-Navier-Stokes system in three dimensions

While finite-time blowup solutions have been studied in depth for the Keller-Segel equation, a fundamental model describing chemotaxis, the existence of finite-time blowup solutions to chemotaxis-fluid models remains largely unexplored. To fill this gap in the literature, we use a quantitative method to directly construct a smooth finite-time blowup solution for the Keller-Segel-Navier-Stokes system with buoyancy in 3D. The heart of the proof is to establish the non-radial finite-codimensional stability of an explicit self-similar blowup solution to 3D Keller-Segel equation with the abstract semigroup tool from [Merle-Raphaël-Rodnianski-Szeftel, 2022], which partially generalizes the radial stability result [Glogić-Schörkhuber, 2024] to the non-radial setting. Additionally, we introduce a robust localization argument to find blowup solutions with non-negative density and finite mass.

math.AP↗

arXiv2025

Detectorless 3D terahertz imaging: achieving subwavelength resolution with reflectance confocal interferometric microscopy

Terahertz imaging holds great potential for non-destructive material inspection, but practical implementation has been limited by resolution constraints. In this study, we present a single-pixel THz imaging system based on a confocal microscope architecture, utilising a quantum cascade laser as both transmitter and phase-sensitive receiver. Our approach integrates laser feedback interferometry detection to achieve a two-fold improvement in lateral resolution and a two-order-of-magnitude enhancement in axial resolution over conventional imaging through precise interferometric phase measurements. This translates to a lateral resolution near $λ/2$ and a depth of focus better than $λ/5$, significantly outperforming traditional confocal systems. The system can produce a 0.5 Mpixel image in under two minutes, surpassing both raster-scanning single-pixel and multipixel focal-plane array-based imagers. Coherent operation enables simultaneous amplitude and phase image acquisition, and a custom visualisation method links amplitude to image saturation and phase to hue, enhancing material characterisation. A 3D tomographic analysis of a silicon chip reveals subwavelength features, demonstrating the system's potential for high-resolution THz imaging and material analysis. This work sets a new benchmark for THz imaging, overcoming key challenges and opening up transformative possibilities for non-destructive material inspection and characterisation.

physics.optics↗

arXiv2025

A non-relativistic theory of quantum mechanics with local modulus symmetry

We have constructed a non-relativistic theory of quantum mechanics based on local modulus symmetry. The resulting connection in the covariant derivative is identified as the escape velocity of the gravitational field. A new real and positive function called the quantum metric function is attached to the complex conjugate of the wave function to satisfy the local modulus symmetry requirement. In an expanding universe, these theoretical features produce new effects that deviate from predictions of conventional quantum mechanics and Newtonian gravity. The quantum metric function yields negligible change for microscopic objects but produces quantum pointer states for macroscopic objects, thus providing a solution to the quantum measurement problem. The time-reversal symmetry is broken in the new quantum kinematics, which has implications for the second law of thermodynamics. The modification of Newtonian gravity is negligible in smaller bound systems but can become significant at the galactic scales. Its potential association with the mass discrepancy problem in the galactic systems is discussed.

gr-qc↗

arXiv2025

Towards a Robust Exclusion of the Sterile-Neutrino Explanation of Short-Baseline Anomalies

The sterile neutrino interpretation of the LSND and MiniBooNE neutrino anomalies is currently being tested at three Liquid Argon detectors: MicroBooNE, SBND, and ICARUS. It has been argued that a degeneracy between $ν_μ\to ν_e$ and $ν_e \to ν_e$ oscillations significantly degrades their sensitivity to sterile neutrinos. Through an independent study, we show two methods to eliminate this concern. First, we resolve this degeneracy by including external constraints on $ν_e$ disappearance from the PROSPECT reactor experiment. Second, by properly analyzing the full three-dimensional parameter space, we demonstrate that the stronger-than-sensitivity exclusion from MicroBooNE alone already covers the entire 2$σ$ preferred regions of MiniBooNE at the level of $2-3σ$. We show that upcoming searches at SBND and ICARUS can improve on this beyond the $4σ$ level, thereby providing a rigorous test of short-baseline anomalies.

hep-ph↗

arXiv2025

SOC-MartNet: A Martingale Neural Network for the Hamilton-Jacobi-Bellman Equation without Explicit inf H in Stochastic Optimal Controls

In this paper, we propose a martingale-based neural network, SOC-MartNet, for solving high-dimensional Hamilton-Jacobi-Bellman (HJB) equations where no explicit expression is needed for the infimum of the Hamiltonian, $\inf_{u \in U} H(t,x,u, z,p)$, and stochastic optimal control problems (SOCPs) with controls on both drift and volatility. We reformulate the HJB equations for the value function by training two neural networks, one for the value function and one for the optimal control with the help of two stochastic processes - a Hamiltonian process and a cost process. The control and value networks are trained such that the associated Hamiltonian process is minimized to satisfy the minimum principle of a feedback SOCP, and the cost process becomes a martingale, thus, ensuring the value function network as the solution to the corresponding HJB equation. Moreover, to enforce the martingale property for the cost process, we employ an adversarial network and construct a loss function characterizing the projection property of the conditional expectation condition of the martingale. Numerical results show that the proposed SOC-MartNet is effective and efficient for solving HJB-type equations and SOCPs with a dimension up to 10,000 in a small number of iteration steps (less than 6000) of training.

math.NA↗

arXiv2025

Time parallelization for hyperbolic and parabolic problems

Time parallelization, also known as PinT (Parallel-in-Time) is a new research direction for the development of algorithms used for solving very large scale evolution problems on highly parallel computing architectures. Despite the fact that interesting theoretical work on PinT appeared as early 1964, it was not until 2004, when processor clock speeds reached their physical limit, that research in PinT took off. A distinctive characteristic of parallelization in time is that information flow only goes forward in time, meaning that time evolution processes seem necessarily to be sequential. Nevertheless, many algorithms have been developed over the last two decades to do PinT computations, and they are often grouped into four basic classes according to how the techniques work and are used: shooting-type methods; waveform relaxation methods based on domain decomposition; multigrid methods in space-time; and direct time parallel methods. However, over the past few years, it has been recognized that highly successful PinT algorithms for parabolic problems struggle when applied to hyperbolic problems. We focus in this survey therefore on this important aspect, by first providing a summary of the fundamental differences between parabolic and hyperbolic problems for time parallelization. We then group PinT algorithms into two basic groups: the first group contains four effective PinT techniques for hyperbolic problems, namely Schwarz Waveform Relaxation with its relation to Tent Pitching; Parallel Integral Deferred Correction; ParaExp; and ParaDiag. While the methods in the first group also work well for parabolic problems, we then present PinT methods especially designed for parabolic problems in the second group: Parareal: the Parallel Full Approximation Scheme in Space-Time; Multigrid Reduction in Time; and Space-Time Multigrid.

math.NA↗

arXiv2025

Hard negative sampling in hyperedge prediction

Hypergraph, which allows each hyperedge to encompass an arbitrary number of nodes, is a powerful tool for modeling multi-entity interactions. Hyperedge prediction is a fundamental task that aims to predict future hyperedges or identify existent but unobserved hyperedges based on those observed. In link prediction for simple graphs, most observed links are treated as positive samples, while all unobserved links are considered as negative samples. However, this full-sampling strategy is impractical for hyperedge prediction, due to the number of unobserved hyperedges in a hypergraph significantly exceeds the number of observed ones. Therefore, one has to utilize some negative sampling methods to generate negative samples, ensuring their quantity is comparable to that of positive samples. In current hyperedge prediction, randomly selecting negative samples is a routine practice. But through experimental analysis, we discover a critical limitation of random selecting that the generated negative samples are too easily distinguishable from positive samples. This leads to premature convergence of the model and reduces the accuracy of prediction. To overcome this issue, we propose a novel method to generate negative samples, named as hard negative sampling (HNS). Unlike traditional methods that construct negative hyperedges by selecting node sets from the original hypergraph, HNS directly synthesizes negative samples in the hyperedge embedding space, thereby generating more challenging and informative negative samples. Our results demonstrate that HNS significantly enhances both accuracy and robustness of the prediction. Moreover, as a plug-and-play technique, HNS can be easily applied in the training of various hyperedge prediction models based on representation learning.

cs.SI↗

arXiv2025

Spin excitations in bilayer La$_3$Ni$_2$O$_7$ superconductors with the interlayer pairing

Prompted by the recent discovery of high-temperature superconductivity in La$_3$Ni$_2$O$_7$ under pressure, this study delves into a theoretical investigation of spin excitations within this intriguing material. Through self-consistent mean-field calculations, we propose that superconductivity in this compound is primarily driven by interlayer pairing mechanisms. This interlayer pairing results in an effective $s_\pm$ pairing gap, with the sign of the pairing gap varying across different Fermi pockets. In the superconducting state, our analysis reveals a striking absence of a spin resonance mode. Moreover, we reveal a spectrum of energy-dependent incommensurate spin excitations. The observed incommensurate structures are elegantly explained by the nesting effect of energy contours, providing a coherent and comprehensive account of experimental observations. The implications of these spin excitations in La$_3$Ni$_2$O$_7$ are profound, offering critical insights into the superconducting mechanism at play. Our results not only contribute to the understanding of this novel superconductor but also pave the way for further research into the interplay between spin dynamics and unconventional superconductivity in layered materials.

cond-mat.supr-con↗

arXiv2025

A Dynamical Variable-separation Method for Parameter-dependent Dynamical Systems

This paper proposes a dynamical Variable-separation method for solving parameter-dependent dynamical systems. To achieve this, we establish a dynamical low-rank approximation for the solutions of these dynamical systems by successively enriching each term in the reduced basis functions via a greedy algorithm. This enables us to reformulate the problem to two decoupled evolution equations at each enrichment step, of which one is a parameter-independent partial differential equation and the other one is a parameter-dependent ordinary differential equation. These equations are directly derived from the original dynamical system and the previously separate representation terms. Moreover, the computational process of the proposed method can be split into an offline stage, in which the reduced basis functions are constructed, and an online stage, in which the efficient low-rank representation of the solution is employed. The proposed dynamical Variable-separation method is capable of offering reduced computational complexity and enhanced efficiency compared to many existing low-rank separation techniques. Finally, we present various numerical results for linear/nonlinear parameter-dependent dynamical systems to demonstrate the effectiveness of the proposed method.

math.NA↗

arXiv2025

Edge-aware Feature Aggregation Network for Polyp Segmentation

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundaries, it is still a challenging task to achieve satisfactory segmentation performance with different scales and shapes. In this study, we present a novel Edge-aware Feature Aggregation Network (EFA-Net) for polyp segmentation, which can fully make use of cross-level and multi-scale features to enhance the performance of polyp segmentation. Specifically, we first present an Edge-aware Guidance Module (EGM) to combine the low-level features with the high-level features to learn an edge-enhanced feature, which is incorporated into each decoder unit using a layer-by-layer strategy. Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation. Further, a Cross-level Fusion Module (CFM) is proposed to effectively integrate the cross-level features, which can exploit the local and global contextual information. Finally, the outputs of CFMs are adaptively weighted by using the learned edge-aware feature, which are then used to produce multiple side-out segmentation maps. Experimental results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and effectiveness. Our implementation code and segmentation maps will be publicly at https://github.com/taozh2017/EFANet.

cs.CV↗

arXiv2025

MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining

Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis. Although recent methods showcase promising achievements, they significantly rely on large-scale private image-text data but pay less attention to the pretraining manner, which limits their further advancements. In this work, we introduce MM-Retinal V2, a high-quality image-text paired dataset comprising CFP, FFA, and OCT image modalities. Then, we propose a novel fundus vision-language pretraining model, namely KeepFIT V2, which is pretrained by integrating knowledge from the elite data spark into categorical public datasets. Specifically, a preliminary textual pretraining is adopted to equip the text encoder with primarily ophthalmic textual knowledge. Moreover, a hybrid image-text knowledge injection module is designed for knowledge transfer, which is essentially based on a combination of global semantic concepts from contrastive learning and local appearance details from generative learning. Extensive experiments across zero-shot, few-shot, and linear probing settings highlight the generalization and transferability of KeepFIT V2, delivering performance competitive to state-of-the-art fundus VLP models trained on large-scale private image-text datasets. Our dataset and model are publicly available via https://github.com/lxirich/MM-Retinal.

cs.CV↗