arXiv subjects

Ming Yuan

Publications and source records attributed to Ming Yuan.

At least 19 recordsLinked to original sources

arXiv2026

Identifiability of Nonnegative Tensor Decompositions via Positive Scattering

Identifiability of tensor decompositions is often established through linear-algebraic conditions on the factor families. For nonnegative decompositions, however, positivity provides additional information that is not captured by dimension and independence alone: nonnegative terms cannot cancel, and their supports constrain competing decompositions. We introduce a positive scattering term that quantifies this additional source of identifiability and combine it with the dimension budget underlying the Lovitz--Petrov generalization of Kruskal's theorem. For every subset of components, we obtain two sufficient conditions: a threshold of $2|S|-2$ guarantees minimality and nonnegative rank, while the stronger threshold $2|S|-1$ guarantees uniqueness among nonnegative decompositions of the same length. The key result is a positive splitting inequality for irreducible exchanges of nonnegative rank-one tensors, which combines the dimension constraint with support-induced geometric rigidity. Although the scattering term is defined through an optimization over intermediate factor spaces, we show that its mode costs are exactly $0$, $1$, or $+\infty$, yielding an exact activation characterization in terms of graph connectivity. The resulting criterion can strictly certify sparse nonnegative tensor decompositions beyond the reach of Kruskal and Lovitz--Petrov conditions, including examples for which those conditions fail even after reshaping. In the matrix case, the two criteria reduce respectively to full-rank factorization and two-sided separability.

stat.ML↗

arXiv2026

Near-Optimal Lower Bounds on One-Bit Compressed Sensing of Approximately Sparse Signals

This paper provides the first near-optimal lower bounds for one-bit compressed sensing of approximately sparse signals lying in a scaled $\ell_1$ ball, which is a commonly adopted relaxation of the exactly $k$-sparse assumption. In prior works, the best known upper bounds on uniform Euclidean error are of order $\widetilde{O}((k/m)^{1/3})$, where $m$ is the number of measurements. Under sub-Gaussian matrices, we establish nearly matching lower bounds for both the canonical one-bit compressed sensing model and the uniformly dithered model. Our argument is to first embed a small Euclidean ball into the signal set, which is straightforward for the dithered model but relies on a lifting map for the canonical model, and then construct two signals in this small ball that are separated in Euclidean distance by at least $(k/m)^{1/3}$ (up to logarithmic factor) but are indistinguishable from the binary measurements. Moreover, our argument extends to approximately sparse signals that live in a properly scaled $\ell_q$ ball $(q\in [0,1])$, yielding a lower bound $\widetilde{\Omega}((k/m)^{\frac{2-q}{2+q}})$ that smoothly bridges the cases of exact sparsity ($q=0$) and $\ell_1$ sparsity ($q=1$). Finally, we discuss the extensions of our lower bounds to sub-Weibull matrices, adversarial bit flipping, matrix recovery, and characterize the transition to the non-sparse case.

cs.IT↗

arXiv2026

Deep Learning for Semen Analysis in Male Infertility: Computer Vision, Multimodal Fusion, and Clinical Translation

Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and assisted reproductive technology. Conventional semen evaluation, however, is labor-intensive, operator-dependent, and limited by inter- and intra-observer variability, motivating the development of objective and reproducible computational approaches. This review provides a comprehensive and perspective-oriented synthesis of artificial intelligence-driven sperm analysis, with a focus on computer vision, deep learning, multimodal fusion, robustness, and clinical translation. We first review task-specific methods for sperm detection and counting, tracking-based motility assessment, semantic and instance segmentation, morphology and defect classification, functional assessment, and genetic integrity evaluation. We then summarize public datasets, benchmarks, evaluation metrics, and emerging multimodal strategies that integrate microscopic images, time-lapse videos, CASA-derived parameters, DNA integrity assays, and clinical metadata. Beyond algorithmic performance, we discuss key barriers to real-world deployment, including data scarcity, cross-center domain shift, annotation inconsistency, interpretability, uncertainty calibration, privacy-preserving learning, and workflow integration. Finally, we outline a staged clinical translation roadmap spanning technical standardization, multicenter retrospective validation, silent prospective evaluation, human-in-the-loop clinical testing, ART outcome validation, regulatory approval, and post-market monitoring. By organizing the field from task-specific visual recognition to trustworthy multimodal reproductive intelligence, this review highlights both the progress and the unresolved challenges required to translate AI-driven sperm analysis into clinically meaningful decision support.

cs.CV↗

arXiv2026

Robust Matrix Estimation with Side Information

We introduce a flexible framework for high-dimensional matrix estimation to incorporate side information for both rows and columns. Existing approaches, such as inductive matrix completion, often impose restrictive structure-for example, an exact low-rank covariate interaction term, linear covariate effects, and limited ability to exploit components explained only by one side (row or column) or by neither-and frequently omit an explicit noise component. To address these limitations, we propose to decompose the underlying matrix as the sum of four complementary components: (possibly nonlinear) interaction between row and column characteristics; row characteristic-driven component, column characteristic-driven component, and residual low-rank structure unexplained by observed characteristics. By combining sieve-based projection with nuclear-norm penalization, each component can be estimated separately and these estimated components can then be aggregated to yield a final estimate. We derive convergence rates that highlight robustness across a range of model configurations depending on the informativeness of the side information. We further extend the method to partially observed matrices under both missing-at-random and missing-not-at-random mechanisms, including block-missing patterns motivated by causal panel data. Simulations and a real-data application to tobacco sales show that leveraging side information improves imputation accuracy and can enhance treatment-effect estimation relative to standard low-rank and spectral-based alternatives.

stat.ME↗

arXiv2026

Knowledge-Embedded Latent Projection for Robust Representation Learning

Latent space models are widely used for analyzing high-dimensional discrete data matrices, such as patient-feature matrices in electronic health records (EHRs), by capturing complex dependence structures through low-dimensional embeddings. However, estimation becomes challenging in the imbalanced regime, where one matrix dimension is much larger than the other. In EHR applications, cohort sizes are often limited by disease prevalence or data availability, whereas the feature space remains extremely large due to the breadth of medical coding system. Motivated by the increasing availability of external semantic embeddings, such as pre-trained embeddings of clinical concepts in EHRs, we propose a knowledge-embedded latent projection model that leverages semantic side information to regularize representation learning. Specifically, we model column embeddings as smooth functions of semantic embeddings via a mapping in a reproducing kernel Hilbert space. We develop a computationally efficient two-step estimation procedure that combines semantically guided subspace construction via kernel principal component analysis with scalable projected gradient descent. We establish estimation error bounds that characterize the trade-off between statistical error and approximation error induced by the kernel projection. Furthermore, we provide local convergence guarantees for our non-convex optimization procedure. Extensive simulation studies and a real-world EHR application demonstrate the effectiveness of the proposed method.

cs.LG↗

arXiv2025

Bacterial turbulence drives interfacial waves and shape dynamics in phase-separated droplets

Liquid-liquid phase separation is important across biology, physics, and materials science. Although usually studied at equilibrium, active components-such as motor proteins, enzymes, and synthetic microswimmers-are increasingly recognized as key players in reshaping phase separation dynamics. Yet how internally generated active stresses are transmitted to capillary interfaces to reshape three-dimensional droplet dynamics remains poorly understood. Here, we encapsulate dense suspensions of motile bacteria inside phase-separated aqueous droplets, creating a closed droplet whose interface is driven from within by bacterial turbulence. By varying bacterial density, we control the active stress at the droplet interface. At low bacterial density, we observe scale-dependent interfacial fluctuations that propagate as waves. In this low Reynolds number regime, these waves arise from an effective inertial response, generated when active bacterial stresses balance passive viscous damping of the interface. At higher bacterial density, droplets deform strongly-exceeding the Plateau-Rayleigh instability threshold-and even form bacteria-scale filaments-a morphology without a passive counterpart. Enhanced droplet motility and accelerated coarsening accompany these shape changes. Our work shows how active stresses can reshape the morphology and dynamics of multiphase systems, offering new insight into the physics of internally driven phase-separated fluids.

cond-mat.soft↗

arXiv2025

Inferential Theory for Pricing Errors with Latent Factors and Firm Characteristics

We study factor models that combine latent factors with firm characteristics and propose a new framework for modeling, estimating, and inferring pricing errors. Following Zhang (2024), our approach decomposes mispricing into two distinct components: inside alpha, explained by firm characteristics but orthogonal to factor exposures, and outside alpha, orthogonal to both factors and characteristics. Our model generalizes those developed recently such as Kelly et al. (2019) and Zhang (2024), resolving issues of orthogonality, basis dependence, and unit sensitivity. Methodologically, we develop estimators grounded in low-rank methods with explicit debiasing, providing closed-form solutions and a rigorous inferential theory that accommodates a growing number of characteristics and relaxes standard assumptions on sample dimensions. Empirically, using U.S. stock returns from 2000-2019, we document strong evidence of both inside and outside alphas, with the former showing industry-level co-movements and the latter reflecting idiosyncratic shocks beyond firm fundamentals. Our framework thus unifies statistical and characteristic-based approaches to factor modeling, offering both theoretical advances and new insights into the structure of pricing errors.

econ.EM↗

arXiv2025

Low-Rank Tensor Estimation from Nonlinear Observations: A Unified Framework

We consider the estimation of a $d_1\times d_2\times d_3$ tensor $X^\star$ of Tucker rank $(r_1,r_2,r_3)$ from the nonlinear observations $\{y_i=f_i(\langle A_i,X^\star\rangle)\}_{i=1}^n$. We develop a unified approach that first constructs a gradient map from the data and then establishes the tensor restricted approximate invertibility condition (T-RAIC), a condition that quantifies how well the gradient map aligns with the ideal descent step under a low-rank tensor dual norm. We show that T-RAIC yields local linear convergence guarantees for a Riemannian gradient descent (RGD) algorithm, which may incorporate a normalization step if $\|X^\star\|_{\rm F}$ is known a priori. Under $O(r_1r_2r_3+\sum_{1\le i\le 3}r_id_i)$ Gaussian measurements, we establish T-RAICs for single-index models, logistic regression, phase retrieval, ReLU regression, and one-bit compressed sensing. The RAICs imply that RGD locally converges to $X^\star$ exactly in phase retrieval and ReLU regression, and up to near-optimal estimation errors in the remaining models. We further show that, in all these models except for phase retrieval, a simple spectral initialization yields the desired initialization from $O(d^{3/2})$ measurements under $d_1=d_2=d_3=d$ (ignoring dependence on the Tucker rank and condition number of $X^\star$). This is also the best known sample complexity for polynomial-time and end-to-end algorithms in tensor linear regression and tensor completion. Numerical simulations are provided to corroborate our theoretical findings.

math.ST↗

arXiv2025

Reconfigurable dissipative entanglement between many spin ensembles: from robust quantum sensing to many-body state engineering

An attractive approach for stabilizing entangled many-body spin states is to employ engineered dissipation. Most existing proposals either target relatively simple collective spin states, or require numerous independent and complex dissipative processes. Here, we show a surprisingly versatile scheme for many-body reservoir engineering that relies solely on fully collective single-excitation decay, augmented with local Hamiltonian terms. Crucially, all these ingredients are readily available in cavity QED setups. Our method is based on splitting the spin system into groups of sub-ensembles, and provides an easily tunable setup for stabilizing a broad family of pure, highly entangled states with closed-form analytic descriptions. Our results have immediate application to multi-ensemble quantum metrology, enabling Heisenberg-limited sensing of field gradients and curvatures. Notably, our approach solves an important challenge in differential quantum sensing by providing the first example of Heisenberg-limited differential sensing immune to common-mode noise and accessible with only simple one-body measurements. The same setup also allows the stabilization of an entire family of entangled states in a 1D chain of spin ensembles with symmetry-protected topological (SPT) order, and have a direct connection to the outputs of sequential unitary circuits. A special case of our protocol efficiently stabilizes the celebrated Affleck-Kennedy-Lieb-Tasaki (AKLT) state.

quant-ph↗

arXiv2025

On Spectral Learning for Odeco Tensors: Perturbation, Initialization, and Algorithms

We study spectral learning for orthogonally decomposable (odeco) tensors, emphasizing the interplay between statistical limits, optimization geometry, and initialization. Unlike matrices, recovery for odeco tensors does not hinge on eigengaps, yielding improved robustness under noise. While iterative methods such as tensor power iterations can be statistically efficient, initialization emerges as the main computational bottleneck. We investigate perturbation bounds, non-convex optimization analysis, and initialization strategies, clarifying when efficient algorithms attain statistical limits and when fundamental barriers remain.

stat.ML↗

arXiv2025

Leveraging LLM Agents for Automated Video Game Testing

Testing MMORPGs (Massively Multiplayer Online Role-Playing Games) is a critical yet labor-intensive task in game development due to their complexity and frequent updating nature. Traditional automated game testing approaches struggle to achieve high state coverage and efficiency in these rich, open-ended environments, while existing LLM-based game-playing approaches are limited to shallow reasoning ability in understanding complex game state-action spaces and long-complex tasks. To address these challenges, we propose TITAN, an effective LLM-driven agent framework for intelligent MMORPG testing. TITAN incorporates four key components to: (1) perceive and abstract high-dimensional game states, (2) proactively optimize and prioritize available actions, (3) enable long-horizon reasoning with action trace memory and reflective self-correction, and (4) employ LLM-based oracles to detect potential functional and logic bugs with diagnostic reports. We implement the prototype of TITAN and evaluate it on two large-scale commercial MMORPGs spanning both PC and mobile platforms. In our experiments, TITAN achieves significantly higher task completion rates (95%) and bug detection performance compared to existing automated game testing approaches. An ablation study further demonstrates that each core component of TITAN contributes substantially to its overall performance. Notably, TITAN detects four previously unknown bugs that prior testing approaches fail to identify. We provide an in-depth discussion of these results, which offer guidance for new avenues of advancing intelligent, general-purpose testing systems. Moreover, TITAN has been deployed in eight real-world game QA pipelines, underscoring its practical impact as an LLM-driven game testing framework.

cs.SE↗

arXiv2025

Large-dimensional Factor Analysis with Weighted PCA

Principal component analysis (PCA) is arguably the most widely used approach for large-dimensional factor analysis. While it is effective when the factors are sufficiently strong, it can be inconsistent when the factors are weak and/or the noise has complex dependence structure. We argue that the inconsistency often stems from bias and introduce a general approach to restore consistency. Specifically, we propose a general weighting scheme for PCA and show that with a suitable choice of weighting matrices, it is possible to deduce consistent and asymptotic normal estimators under much weaker conditions than the usual PCA. While the optimal weight matrix may require knowledge about the factors and covariance of the idiosyncratic noise that are not known a priori, we develop an agnostic approach to adaptively choose from a large class of weighting matrices that can be viewed as PCA for weighted linear combinations of auto-covariances among the observations. Theoretical and numerical results demonstrate the merits of our methodology over the usual PCA and other recently developed techniques for large-dimensional approximate factor models.

stat.ME↗

arXiv2025

Efficient benchmarking of logical magic state

High-fidelity logical magic states are a critical resource for fault-tolerant quantum computation, enabling non-Clifford logical operations through state injection. However, benchmarking these states presents significant challenges: one must estimate the infidelity $\epsilon$ with multiplicative precision, while many quantum error-correcting codes only permit Clifford operations to be implemented fault-tolerantly. Consequently, conventional state tomography requires $\sim1/\epsilon^2$ samples, making benchmarking impractical for high-fidelity states. In this work, we show that any benchmarking scheme measuring one copy of the magic state per round necessarily requires $\Omega(1/\epsilon^2)$ samples for single-qubit magic states. We then propose two approaches to overcome this limitation: (i) Bell measurements on two copies of the twirled state and (ii) single-copy schemes leveraging twirled multi-qubit magic states. Both benchmarking schemes utilize measurements with stabilizer states orthogonal to the ideal magic state and we show that $O(1/\epsilon)$ sample complexity is achieved, which we prove to be optimal. Finally, we demonstrate the robustness of our protocols through numerical simulations under realistic noise models, confirming that their advantage persists even at moderate error rates currently achievable in state-of-the-art experiments.

quant-ph↗

arXiv2025

Enhancing Microwave-Optical Bell Pairs Generation for Quantum Transduction Using Kerr Nonlinearity

Microwave-optical quantum transduction can be achieved via quantum teleportation using microwave-optical photon Bell pairs. The standard spontaneous parametric down-conversion (SPDC) has to trade off between generation fidelity and probability due to unwanted higher-excitation pairs in the output. In this work, we propose a pulsed SPDC scheme that employs strong Kerr nonlinearity in the microwave mode. This nonlinearity causes significant detuning of higher excitations due to the anharmonicity of energy levels, and the system can be pulse-driven to produce single-photon pairs in the output. Our pulsed nonlinear approach can generate high-fidelity Bell pairs with high probability, alleviating the trade-off between fidelity and probability inherent in traditional SPDC schemes. We optimize both the pulse width and driving strength, demonstrating that our protocol outperforms the SPDC scheme in a realistic setting of finite nonlinearity and intrinsic photon loss.

quant-ph↗

arXiv2025

TransST: Transfer Learning Embedded Spatial Factor Modeling of Spatial Transcriptomics Data

Background: Spatial transcriptomics have emerged as a powerful tool in biomedical research because of its ability to capture both the spatial contexts and abundance of the complete RNA transcript profile in organs of interest. However, limitations of the technology such as the relatively low resolution and comparatively insufficient sequencing depth make it difficult to reliably extract real biological signals from these data. To alleviate this challenge, we propose a novel transfer learning framework, referred to as TransST, to adaptively leverage the cell-labeled information from external sources in inferring cell-level heterogeneity of a target spatial transcriptomics data. Results: Applications in several real studies as well as a number of simulation settings show that our approach significantly improves existing techniques. For example, in the breast cancer study, TransST successfully identifies five biologically meaningful cell clusters, including the two subgroups of cancer in situ and invasive cancer; in addition, only TransST is able to separate the adipose tissues from the connective issues among all the studied methods. Conclusions: In summary, the proposed method TransST is both effective and robust in identifying cell subclusters and detecting corresponding driving biomarkers in spatial transcriptomics data.

q-bio.GN↗

arXiv2025

DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model

The depth completion task is a critical problem in autonomous driving, involving the generation of dense depth maps from sparse depth maps and RGB images. Most existing methods employ a spatial propagation network to iteratively refine the depth map after obtaining an initial dense depth. In this paper, we propose DenseFormer, a novel method that integrates the diffusion model into the depth completion task. By incorporating the denoising mechanism of the diffusion model, DenseFormer generates the dense depth map by progressively refining an initial random depth distribution through multiple iterations. We propose a feature extraction module that leverages a feature pyramid structure, along with multi-layer deformable attention, to effectively extract and integrate features from sparse depth maps and RGB images, which serve as the guiding condition for the diffusion process. Additionally, this paper presents a depth refinement module that applies multi-step iterative refinement across various ranges to the dense depth results generated by the diffusion process. The module utilizes image features enriched with multi-scale information and sparse depth input to further enhance the accuracy of the predicted depth map. Extensive experiments on the KITTI outdoor scene dataset demonstrate that DenseFormer outperforms classical depth completion methods.

cs.CV↗

arXiv2025

Fast Sideband Control of a Weakly Coupled Multimode Bosonic Memory

Circuit quantum electrodynamics (cQED) with superconducting cavities coupled to nonlinear circuits like transmons offers a promising platform for hardware-efficient quantum information processing. We address critical challenges in realizing this architecture by weakening the dispersive coupling while also demonstrating fast, high-fidelity multimode control by dynamically amplifying gate speeds through transmon-mediated sideband interactions. This approach enables transmon-cavity SWAP gates, for which we achieve speeds up to 30 times larger than the bare dispersive coupling. Combined with transmon rotations, this allows for efficient, universal state preparation in a single cavity mode, though achieving unitary gates and extending control to multiple modes remains a challenge. In this work, we overcome this by introducing two sideband control strategies: (1) a shelving technique that prevents unwanted transitions by temporarily storing populations in sideband-transparent transmon states and (2) a method that exploits the dispersive shift to synchronize sideband transition rates across chosen photon-number pairs to implement transmon-cavity SWAP gates that are selective on photon number. We leverage these protocols to prepare Fock and binomial code states across any of ten modes of a multimode cavity with millisecond cavity coherence times. We demonstrate the encoding of a qubit from a transmon into arbitrary vacuum and Fock state superpositions, as well as entangled NOON states of cavity mode pairs\textemdash a scheme extendable to arbitrary multimode Fock encodings. Furthermore, we implement a new binomial encoding gate that converts arbitrary transmon superpositions into binomial code states in $\qty{4}{\micro\second}$ (less than $1/\chi$), achieving an average post-selected final state fidelity of $\qty{96.3}{\percent}$ across different fiducial input states.

quant-ph↗

arXiv2025

Efficient quantum tomography of a polynomial subspace

Quantum tomography is crucial for characterizing the quantum states of multipartite systems, but its practicality is often limited by the exponentially large dimension of the Hilbert space. Most existing approaches, such as compressed sensing and tensor network-based tomography, impose structural constraints on the state to enable more resource-efficient characterization. However, not all physical states can be well-approximated with highly structured states. Here, we develop a partial quantum tomography method based on direct fidelity estimation (DFE) that focuses on a neighborhood subspace -- the subspace spanned by states physically close to a given target state. Using this generalized DFE method, we estimate elements of the density operator within this subspace in a self-verifying manner. We investigate the efficiency of this approach under different sets of available measurements for various states and find that the set of available measurements significantly impacts the cost of DFE. For example, we show that Pauli measurements alone are insufficient for performing efficient DFE on all product states, whereas the full set of product measurements is sufficient. This method can be applied in many situations, including characterizing quantum systems with confined dynamics and verifying preparations of quantum states and processes.

quant-ph↗