arXiv subjects

Hang Qi

Publications and source records attributed to Hang Qi.

At least 19 recordsLinked to original sources

arXiv2026

A persistent-homology-Gaussian prior for solving infinite-dimensional Bayesian inverse scattering problems

Bayesian inference methods have been developed to address inverse problems in function spaces where the unknown parameters are of infinite dimension. However, conventional Gaussian priors remain inadequate for reconstructing discontinuous or sharply varying target functions encountered in practical applications like obstacle reconstruction. Although hybrid priors have emerged as a promising solution, significant challenges remain in developing theoretically rigorous and computationally tractable frameworks in engineering applications. To address these issues, we propose a persistent-homology-Gaussian (PHG) prior for solving the acoustic obstacle scattering inverse problem in the infinite-dimensional Bayesian setting, which combines a weighted persistence-based regularization term with a periodic Gaussian reference measure through a Gibbs tilt. Then, the complex boundary is represented by a log-radial function on the unit circle, so that the reconstruction from far-field data is formulated as a function-space inverse problem. The well-posedness of the resulting posterior measure is established in the Hellinger, total variation, and Wasserstein-$p$ metrics. Furthermore, the convergence of finite-dimensional posterior approximations is obtained, and posterior sampling is performed by a preconditioned Crank--Nicolson (pCN) method. Numerical experiments show that the proposed PHG prior yields accurate and stable reconstructions under more extensive noisy conditions, providing explicit control of multiscale topological features and better performance compared to other conventional priors.

math.NA↗

arXiv2026

Matrix-Free FFT-HSS Preconditioning for Periodic Landau-Lifshitz-Gilbert Saddle-Point Systems

In this paper, a matrix-free Hermitian/skew-Hermitian splitting (HSS) preconditioner is proposed for periodic Landau--Lifshitz--Gilbert (LLG) saddle-point systems. The main contributions are threefold. (1) The coupled skew/constraint block has an explicit $4\times4$ nodal inverse and requires no local factorization. Combining this local inverse with FFT inversion of the shifted exchange block gives $O(N_g\log N_g)$ work and $O(N_g)$ temporary storage per application. (2) We establish well-posedness and an even-step GMRES residual bound, with an iteration estimate uniform in the mesh size and time step for a class of coupled refinements. (3) The projected implicit Euler and projection-free Crank--Nicolson-type midpoint discretizations generate saddle-point systems of the same form, so the same matrix-free FFT--HSS preconditioning procedure applies to both. They achieve first- and second-order temporal accuracy, respectively; for quadratic-affine energies, the midpoint scheme also preserves nodal length and satisfies an exact discrete dissipation identity. Two- and three-dimensional experiments confirm the predicted temporal orders and midpoint invariants. On the largest smooth tests, FFT--HSS reduces GMRES iterations by $35$--$39\%$ relative to same-grid Householder preconditioning and yields approximately 15-fold and 3-fold speedups over unpreconditioned GMRES in two and three dimensions, respectively. Broadband tests retain mesh-independent iterations in the covered refinement regime at time steps 13.3 times the linearized explicit exchange limit.

math.NA↗

arXiv2026

Single-Photon Sensitive Optoelectronic Fibres for Distributed Nuclear Radiation Detection in Textile Fabrics

Nuclear radiation detectors play a key role in applications spanning nuclear and particle physics, nuclear engineering, security, and medicine. With the expanded global interest in nuclear power, discreet, inconspicuous, and readily deployable nuclear detection capabilities are increasingly important. However, conventional dosimeters are often rigid, bulky, or lack spatial resolution, limiting their use for mobile, conformal, or large-area distributed mapping of dynamic fields. Here, we present flexible, radiation-sensitive optoelectronic fibres with up to 50% elasticity for real-time gamma dosimetry. Silicon photomultipliers are thermally drawn into the core of fibres composed of a scintillator waveguide, enabling electronic-photonic integration and detection of scintillation light with single-photon resolution. We show that these fibres are sensitive to localized nuclear radiation exposure from collimated 0.5 {\mu}Ci Sr-90 {\beta}-sources and 10 {\mu}Ci Cs-137 and Co-60 {\gamma}-sources, with extended responsivity measured over 30 cm, and estimated lower detection limits approaching near- background radiation levels (~14-41 nSv/hr). Co-locating the scintillator and detectors in the fibre eliminates past length limitations driven by optical losses and enabling a greater collection cone through capture of transient non- guided modes. We further enhance radiation sensitivity and mechanical robustness by covering the fibres with a tungsten-merino wool composite braid, enabling us to machine-weave them into fabrics alongside common textile yarns. The tungsten wires function as a gamma-electron converter, increasing the detection efficiency of the assembly by ~20%. Distributed woven arrays of fibres formed in this way present an opportunity to create large-area, conformal fabrics capable of real- time dosimetry of gamma radiation fields with high spatial resolution.

physics.ins-det↗

arXiv2026

Long Range Outlook for Short-Range Correlations

Short range correlated (SRC) N N pairs are pairs of nucleons with high relative momentum (prel > kF where kF ~ 250 MeV/c is the Fermi momentum in medium to heavy nuclei) and lower center of mass momentum. The motivation for studying SRC pairs ranges from a desire to achieve a more comprehensive understanding of the many-body nuclear wave-function at high-resolution to searching for explicit QCD-dynamics effects within the nuclear medium, not to mention connections to many other open problems in nuclear physics. Exploring short-range correlations was one of the physics motivations for building CEBAF (now Jefferson Lab). Scientists used the high luminosity and high energy of this cutting-edge machine to find kinematics that cleanly showed the signals of short-range correlations. This paved the way in the last two decades for tremendous progress understanding these correlations. This paper reviews recent progress and highlights outstanding questions and areas that need further study.

nucl-ex↗

arXiv2025

Cluster-Based Client Selection for Dependent Multi-Task Federated Learning in Edge Computing

We study the client selection problem in Federated Learning (FL) within mobile edge computing (MEC) environments, particularly under the dependent multi-task settings, to reduce the total time required to complete various learning tasks. We propose CoDa-FL, a Cluster-oriented and Dependency-aware framework designed to reduce the total required time via cluster-based client selection and dependent task assignment. Our approach considers Earth Mover's Distance (EMD) for client clustering based on their local data distributions to lower computational cost and improve communication efficiency. We derive a direct and explicit relationship between intra-cluster EMD and the number of training rounds required for convergence, thereby simplifying the otherwise complex process of obtaining the optimal solution. Additionally, we incorporate a directed acyclic graph-based task scheduling mechanism to effectively manage task dependencies. Through numerical experiments, we validate that our proposed CoDa-FL outperforms existing benchmarks by achieving faster convergence, lower communication and computational costs, and higher learning accuracy under heterogeneous MEC settings.

cs.LG↗

arXiv2025

Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning

We consider the client selection problem in wireless Federated Learning (FL), with the objective of reducing the total required time to achieve a certain level of learning accuracy. Since the server cannot observe the clients' dynamic states that can change their computation and communication efficiency, we formulate client selection as a restless multi-armed bandit problem. We propose a scalable and efficient approach called the Whittle Index Learning in Federated Q-learning (WILF-Q), which uses Q-learning to adaptively learn and update an approximated Whittle index associated with each client, and then selects the clients with the highest indices. Compared to existing approaches, WILF-Q does not require explicit knowledge of client state transitions or data distributions, making it well-suited for deployment in practical FL settings. Experiment results demonstrate that WILF-Q significantly outperforms existing baseline policies in terms of learning efficiency, providing a robust and efficient approach to client selection in wireless FL.

cs.LG↗

arXiv2025

Multiphysics embedding localized orthogonal decomposition for thermomechanical coupling problems

Multiscale thermomechanical problems in highly heterogeneous media are challenging because the elastic, thermal, and coupling coefficients may vary on unresolved spatial scales. We propose a multiphysics-embedding localized orthogonal decomposition (ME-LOD) method in which displacement and temperature correctors are generated by a coupled static operator. The corrector problems are localized to coarse-grid patches and solved in the kernel of a projective quasi-interpolation operator. We prove uniform inf-sup stability on the global fine-scale kernel and on all zero-extension patch kernels, establish exponential decay of the coupled correctors and the resulting multiscale basis functions, and derive spatial approximation and fully discrete reduction estimates. Numerical experiments demonstrate that, for the tested periodic, random, and high-contrast coefficient fields, ME-LOD attains smaller errors than the comparison method at the same coarse resolution and patch size and can reach a prescribed accuracy with fewer oversampling layers. Although each coupled local corrector is more expensive than a decoupled corrector, the improved localization yields a favorable overall accuracy-to-cost balance in the reported tests.

math.NA↗

arXiv2025

HALO: Human-Aligned End-to-end Image Retargeting with Layered Transformations

Image retargeting aims to change the aspect-ratio of an image while maintaining its content and structure with less visual artifacts. Existing methods still generate many artifacts or fail to maintain original content or structure. To address this, we introduce HALO, an end-to-end trainable solution for image retargeting. Since humans are more sensitive to distortions in salient areas than non-salient areas of an image, HALO decomposes the input image into salient/non-salient layers and applies different wrapping fields to different layers. To further minimize the structure distortion in the output images, we propose perceptual structure similarity loss which measures the structure similarity between input and output images and aligns with human perception. Both quantitative results and a user study on the RetargetMe dataset show that HALO achieves SOTA. Especially, our method achieves an 18.4% higher user preference compared to the baselines on average.

cs.CV↗

arXiv2024

Imagen 3

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

cs.CV↗

arXiv2024

Two-scale Analysis for Multiscale Landau-Lifshitz-Gilbert Equation: Theory and Numerical Methods

This paper discusses the theory and numerical method of two-scale analysis for the multiscale Landau-Lifshitz-Gilbert equation in composite ferromagnetic materials. The novelty of this work can be summarized in three aspects: Firstly, the more realistic and complex model is considered, including the effects of the exchange field, anisotropy field, stray field, and external magnetic field. The explicit convergence orders in the $H^1$ norm between the classical solution and the two-scale solution are obtained. Secondly, we propose a robust numerical framework, which is employed in several comprehensive experiments to validate the convergence results for the Periodic and Neumann problems. Thirdly, we design an improved implicit numerical scheme to reduce the required number of iterations and relaxes the constraints on the time step size, which can significantly improve computational efficiency. Specifically, the projection and the expansion methods are given to overcome the inherent non-consistency in the initial data between the multiscale problem and homogenized problem.

math.NA↗

arXiv2023

Federated Learning of Shareable Bases for Personalization-Friendly Image Classification

Personalized federated learning (PFL) aims to harness the collective wisdom of clients' data while building personalized models tailored to individual clients' data distributions. Existing works offer personalization primarily to clients who participate in the FL process, making it hard to encompass new clients who were absent or newly show up. In this paper, we propose FedBasis, a novel PFL framework to tackle such a deficiency. FedBasis learns a set of few shareable ``basis'' models, which can be linearly combined to form personalized models for clients. Specifically for a new client, only a small set of combination coefficients, not the model weights, needs to be learned. This notion makes FedBasis more parameter-efficient, robust, and accurate than competitive PFL baselines, especially in the low data regime, without increasing the inference cost. To demonstrate the effectiveness and applicability of FedBasis, we also present a more practical PFL testbed for image classification, featuring larger data discrepancies across clients in both the image and label spaces as well as more faithful training and test splits.

cs.CV↗

arXiv2023

Investigating and modeling day-to-day route choices based on laboratory experiments. Part II: A route-dependent attraction-based stochastic process model

To explain day-to-day (DTD) route-choice behaviors and traffic dynamics observed in a series of lab experiments, Part I of this research proposed a discrete choice-based analytical dynamic model (Qi et al., 2023). Although the deterministic model could well reproduce the experimental observations, it converges to a stable equilibrium of route flow while the observed DTD evolution is apparently with random oscillations. To overcome the limitation, the paper proposes a route-dependent attraction-based stochastic process (RDAB-SP) model based on the same behavioral assumptions in Part I of this research. Through careful comparison between the model-based estimation and experimental observations, it is demonstrated that the proposed RDAB-SP model can accurately reproduce the random oscillations both in terms of flow switching and route flow evolution. To the best of our knowledge, this is the first attempt to explain and model experimental observations by using stochastic process DTD models, and it is interesting to find that the seemingly unanticipated phenomena (i.e., random route switching behavior) is actually dominated by simple rules, i.e., independent and probability-based route-choice behavior. Finally, an approximated model is developed to help simulate the stochastic process and evaluate the equilibrium distribution in a simple and efficient manner, making the proposed model a useful and practical tool in transportation policy design.

physics.soc-ph↗

arXiv2023

Investigating day-to-day route choices based on multi-scenario laboratory experiments. Part I: Route-dependent attraction and its modeling

In the area of urban transportation networks, a growing number of day-to-day (DTD) traffic dynamic theories have been proposed to describe the network flow evolution, and an increasing amount of laboratory experiments have been conducted to observe travelers' behavior regularities. However, the "communication" between theorists and experimentalists has not been made well. This paper devotes to 1) detecting unanticipated behavior regularities by conducting a series of laboratory experiments, and 2) improving existing DTD dynamics theories by embedding the observed behavior regularities into a route choice model. First, 312 subjects participated in one of the eight decision-making scenarios and make route choices repeatedly in congestible parallel-route networks. Second, three route-switching behavior patterns that cannot be fully explained by the classic route-choice models are observed. Third, to enrich the explanation power of a discrete route-choice model, behavioral assumptions of route-dependent attractions, i.e., route-dependent inertia and preference, are introduced. An analytical DTD dynamic model is accordingly proposed and proven to steadily converge to a unique equilibrium state. Finally, the proposed DTD model could satisfactorily reproduce the observations in various datasets. The research results can help transportation science theorists to make the best use of laboratory experimentation and to build network equilibrium or DTD dynamic models with both real behavioral basis and neat mathematical properties.

physics.soc-ph↗

arXiv2022

Efficient Image Representation Learning with Federated Sampled Softmax

Learning image representations on decentralized data can bring many benefits in cases where data cannot be aggregated across data silos. Softmax cross entropy loss is highly effective and commonly used for learning image representations. Using a large number of classes has proven to be particularly beneficial for the descriptive power of such representations in centralized learning. However, doing so on decentralized data with Federated Learning is not straightforward as the demand on FL clients' computation and communication increases proportionally to the number of classes. In this work we introduce federated sampled softmax (FedSS), a resource-efficient approach for learning image representation with Federated Learning. Specifically, the FL clients sample a set of classes and optimize only the corresponding model parameters with respect to a sampled softmax objective that approximates the global full softmax objective. We examine the loss formulation and empirically show that our method significantly reduces the number of parameters transferred to and optimized by the client devices, while performing on par with the standard full softmax method. This work creates a possibility for efficiently learning image representations on decentralized data with a large number of classes under the federated setting.

cs.LG↗

arXiv2022

FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients

In classical federated learning, the clients contribute to the overall training by communicating local updates for the underlying model on their private data to a coordinating server. However, updating and communicating the entire model becomes prohibitively expensive when resource-constrained clients collectively aim to train a large machine learning model. Split learning provides a natural solution in such a setting, where only a small part of the model is stored and trained on clients while the remaining large part of the model only stays at the servers. However, the model partitioning employed in split learning introduces a significant amount of communication cost. This paper addresses this issue by compressing the additional communication using a novel clustering scheme accompanied by a gradient correction method. Extensive empirical evaluations on image and text benchmarks show that the proposed method can achieve up to $490\times$ communication cost reduction with minimal drop in accuracy, and enables a desirable performance vs. communication trade-off.

cs.LG↗

arXiv2021

Federated Multi-Target Domain Adaptation

Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy. However, it is not always feasible to obtain high-quality supervisory signals from users, especially for vision tasks. Unlike typical federated settings with labeled client data, we consider a more practical scenario where the distributed client data is unlabeled, and a centralized labeled dataset is available on the server. We further take the server-client and inter-client domain shifts into account and pose a domain adaptation problem with one source (centralized server data) and multiple targets (distributed client data). Within this new Federated Multi-Target Domain Adaptation (FMTDA) task, we analyze the model performance of exiting domain adaptation methods and propose an effective DualAdapt method to address the new challenges. Extensive experimental results on image classification and semantic segmentation tasks demonstrate that our method achieves high accuracy, incurs minimal communication cost, and requires low computational resources on client devices.

cs.CV↗

arXiv2021

A Field Guide to Federated Optimization

Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance. The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications.

cs.LG↗

arXiv2021

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

cs.LG↗