arXiv subjects

Yao Wang

Publications and source records attributed to Yao Wang.

At least 127 records · Page 7Linked to original sources

arXiv2025

Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning

Purpose: This work proposes a novel self-supervised noise-adaptive image denoising framework, called Repetition to Repetition (Rep2Rep) learning, for low-field (<1T) MRI applications. Methods: Rep2Rep learning extends the Noise2Noise framework by training a neural network on two repeated MRI acquisitions, using one repetition as input and another as target, without requiring ground-truth data. It incorporates noise-adaptive training, enabling denoising generalization across varying noise levels and flexible inference with any number of repetitions. Performance was evaluated on both synthetic noisy brain MRI and 0.55T prostate MRI data, and compared against supervised learning and Monte Carlo Stein's Unbiased Risk Estimator (MC-SURE). Results: Rep2Rep learning outperforms MC-SURE on both synthetic and 0.55T MRI datasets. On synthetic brain data, it achieved denoising quality comparable to supervised learning and surpassed MC-SURE, particularly in preserving structural details and reducing residual noise. On the 0.55T prostate MRI dataset, a reader study showed radiologists preferred Rep2Rep-denoised 2-average images over 8-average noisy images. Rep2Rep demonstrated robustness to noise-level discrepancies between training and inference, supporting its practical implementation. Conclusion: Rep2Rep learning offers an effective self-supervised denoising for low-field MRI by leveraging routinely acquired multi-repetition data. Its noise-adaptivity enables generalization to different SNR regimes without clean reference images. This makes Rep2Rep learning a promising tool for improving image quality and scan efficiency in low-field MRI.

eess.IV↗

arXiv2025

Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction

Magnetic resonance imaging (MRI) reconstruction has largely been dominated by deep neural networks (DNN); however, many state-of-the-art architectures use black-box structures, which hinder interpretability and improvement. Here, we propose an interpretable DNN architecture for self-supervised MRI reconstruction and denoising by directly parameterizing and learning the classical primal-dual splitting, dubbed LPDSNet. This splitting algorithm allows us to decouple the observation model from the signal prior. Experimentally, we show other interpretable architectures without this decoupling property exhibit failure in the self-supervised learning regime. We report state-of-the-art self-supervised joint MRI reconstruction and denoising performance and novel noise-level generalization capabilities, where in contrast black-box networks fail to generalize.

eess.IV↗

arXiv2025

Detecting Thermodynamic Phase Transition via Explainable Machine Learning of Photoemission Spectroscopy

Identifying thermodynamic signatures of electronic phases, such as superconductivity, is challenging in low-dimensional materials due to strong fluctuations and low probing volume. Spectroscopic methods are often used to identify new bulk phases, but their main measurable quantity -- electronic energy gaps -- is no longer an effective order parameter in low-dimensional and fluctuating systems. Combining angle-resolved photoemission with a domain-adversarial neural network, we report a data-driven method to identify thermodynamic phase transitions solely based on single-particle spectra. We demonstrate 97.6$\%$ accuracy in cuprate superconductor Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ with strong superconducting fluctuations. This model notably compensates for the scarcity of experimental data by leveraging virtually inexhaustible simulated data. Further, its explainability reveals the crucial role of in-gap spectral weight in detecting phase fluctuations and thermodynamic transitions. Our work pinpoints the spectroscopic signatures of fluctuating orders and enables using spectroscopy for machine-learning-assisted material discovery for low-dimensional and strong coupling systems.

cond-mat.supr-con↗

arXiv2025

Extended dissipaton theory with application to adatom-graphene composite

In this paper, we present the extended dissipaton theory, including the dissipaton-equation-of-motion formalism and the equivalent dissipaton-embedded quantum master equation. These are exact, non-Markovian, and non-perturbative theories, capable of handling not only linear but also quadratic environmental couplings. These scenarios are prevalent in a variety of strongly correlated electronic systems, including mesoscopic nanodevices and superconductors. As a demonstration, we apply the present theory to simulate the spectral functions of an adatom on a graphene substrate. We analyze the spectral peaks in the presence of the graphene substrate and compare them to those obtained in conventional metal environments. The adatom's spectral functions reveal intricate behaviors arising from the band structure of graphene.

cond-mat.mes-hall↗

arXiv2025

Distillation Improves Visual Place Recognition for Low Quality Images

Real-time visual localization often utilizes online computing, for which query images or videos are transmitted to remote servers for visual place recognition (VPR). However, limited network bandwidth necessitates image-quality reduction and thus the degradation of global image descriptors, reducing VPR accuracy. We address this issue at the descriptor extraction level with a knowledge-distillation methodology that learns feature representations from high-quality images to extract more discriminative descriptors from low-quality images. Our approach includes the Inter-channel Correlation Knowledge Distillation (ICKD) loss, Mean Squared Error (MSE) loss, and Triplet loss. We validate the proposed losses on multiple VPR methods and datasets subjected to JPEG compression, resolution reduction, and video quantization. We obtain significant improvements in VPR recall rates under all three tested modalities of lowered image quality. Furthermore, we fill a gap in VPR literature on video-based data and its influence on VPR performance. This work contributes to more reliable place recognition in resource-constrained environments.

cs.CV↗

arXiv2025

Memory Kernel Coupling Theory: Obtain Time Correlation Function from Higher-order Moments

Dynamical observables can often be described by time correlation functions (TCFs). However, efficiently calculating TCFs for complex quantum systems is a significant challenge, which generally requires solving the full dynamics of the systems. This Letter presents the memory kernel coupling theory (MKCT), a general formalism for evaluating TCFs. The MKCT builds upon Mori's memory kernel formalism for TCFs. Our theory further decomposes the memory kernel into auxiliary kernels. Rapid decay of auxiliary kernels allows us to truncate the coupled equations of motion with high accuracy. Notably, only higher-order moments are sufficient as the input for obtaining TCFs. While this formalism is general, we carry out the numerical demonstration for a typical open quantum system--the spin-boson model.

physics.chem-ph↗

arXiv2025

Entanglement Witness for Indistinguishable Electrons using Solid-State Spectroscopy

Characterizing entanglement in quantum materials is crucial for advancing next-generation quantum technologies. Despite recent strides in witnessing entanglement in magnetic materials with distinguishable spin modes, quantifying entanglement in systems formed by indistinguishable electrons remains a formidable challenge. To solve this problem, we introduce a method to extract various four-fermion correlations by analyzing the nonlinearity in resonant inelastic x-ray scattering spectra. These correlations constitute the primary components of the cumulant two-particle reduced density matrix. We further derive bounds for its eigenvalues and demonstrate the linear scaling with fermionic entanglement depth, providing a reliable witness for entanglement. Using the material-relevant strongly correlated models as examples, we show how this entanglement witness can efficiently quantify multipartite entanglement across different phase regions, highlighting its advantage over quantum Fisher information.

cond-mat.str-el↗

arXiv2025

U-Motion: Learned Point Cloud Video Compression with U-Structured Temporal Context Generation

Point cloud video (PCV) is a versatile 3D representation of dynamic scenes with emerging applications. This paper introduces U-Motion, a learning-based compression scheme for both PCV geometry and attributes. We propose a U-Structured inter-frame prediction framework, U-Inter, which performs explicit motion estimation and compensation (ME/MC) at different scales with varying levels of detail. It integrates Top-Down (Fine-to-Coarse) Motion Propagation, Bottom-Up Motion Predictive Coding and Multi-scale Group Motion Compensation to enable accurate motion estimation and efficient motion compression at each scale. In addition, we design a multi-scale spatial-temporal predictive coding module to capture the cross-scale spatial redundancy remaining after U-Inter prediction. We conduct experiments following the MPEG Common Test Condition for dense dynamic point clouds and demonstrate that U-Motion can achieve significant gains over MPEG G-PCC-GesTM v3.0 and recently published learning-based methods for both geometry and attribute compression.

cs.CV↗

arXiv2025

Searching for Low-Redshift Hot Dust-Obscured Galaxies

Hot Dust-Obscured Galaxies (Hot DOGs), discovered by the "W1W2 dropout" selection at high redshifts ($z\sim$ 2-4), are a rare population of hyper-luminous obscured quasars. Their number density is comparable to similarly luminous type 1 quasars in the same redshift range, potentially representing a short, yet critical stage in galaxy evolution. The evolution in their number density towards low redshift, however, remains unclear as their selection function is heavily biased against objects at $z\lesssim2$. We combine data from the WISE and Herschel archives to search for Hot DOGs at $z<0.5$ based on their unique spectral energy distributions. We find 68 candidates, and spectroscopic observations confirm that 3 of them are at $z<0.5$. For those 3 we find their black hole accretion is close to the Eddington limit, with lower bolometric luminosities and black hole masses than those of higher-$z$ Hot DOGs. Compared to high-$z$ systems, these low-$z$ systems are closer to the local relation between host galaxy stellar mass and black hole mass but still lie above it, and we discuss several possible scenarios for it. Finally, we also find the surface number density of $z<$0.5 Hot DOGs is $\rm 2.4 \times 10^{-3}$ deg$^{-2}$, about an order of magnitude lower than high-$z$ Hot DOGs but comparable to hyper-luminous unobscured quasars in the same redshift range. These results further support the idea that Hot DOGs may be a transitional phase of galaxy evolution.

astro-ph.GA↗

arXiv2025

Doping dependence of 2-spinon excitations in the doped 1D cuprate Ba$_2$CuO$_{3+δ}$

Recent photoemission experiments on the quasi-one-dimensional Ba-based cuprates suggest that doped holes experience an attractive potential not captured using the simple Hubbard model. This observation has garnered significant attention due to its potential relevance to Cooper pair formation in high-$T_c$ cuprate superconductors. To scrutinize this assertion, we examined signatures of such an attractive potential in doped 1D cuprates Ba$_2$CuO$_{3+δ}$ by measuring the dispersion of the 2-spinon excitations using Cu $L_3$-edge resonant inelastic X-ray scattering (RIXS). Upon doping, the 2-spinon excitations appear to weaken, with a shift of the minimal position corresponding to the nesting vector of the Fermi points, $q_F$. Notably, we find that the energy scale of the 2-spinons near the Brillouin zone boundary is substantially softened compared to that predicted by the Hubbard model in one-dimension. Such a discrepancy implies missing ingredients, which lends support for the presence of an additional attractive potential between holes.

cond-mat.str-el↗

arXiv2025

Simulations for the evolution of the chemical clock HC3N/N2H+ in high-mass star-forming regions

From observations, column density ratios or integrated intensity ratios between some species exhibit monotonic increase or decrease along with the evolution of high-mass star-forming regions (HMSFRs). Such ratios are defined as chemical clocks, which can be used to constrain the evolutionary stage. We performed chemical simulations to reproduce the observed column density ratio of HC3N/N2H+ and the abundances of these two species across various evolutionary stages in HMSFRs. Simultaneously, we identified the chemical processes responsible for the observed time-dependent trends in these stages. Our simulations utilized the astrochemical code Nautilus and the existing 1D models of HMSFRs that cover four evolutionary stages, accompanied by variations in density and temperature throughout the entire evolution. When averaging over large spatial scales, the best model produced successfully matches the observed column density ratio of HC3N/N2H+ and the abundances of the species involved at specific times for each evolutionary stage; that is, the late high-mass starless core stage, the early high-mass protostellar object stage, and the early ultracompact HII stage. HC3N is mainly affected by the warm carbon-chain chemistry (WCCC) and its own thermal desorption, while N2H+ is primarily influenced by the thermal desorption of N2, CO, CH4, NH3, and H2O followed by dissociative recombination and ion-molecule reactions. The results obtained from the best-fitting model timescales broadly agree with statistical estimates. Based on our best-fit model, we further examined other 350 ratios involving 27 species, and 178 ratios exhibit an increasing or decreasing evolutionary trend around the best-fit timescales of HC3N/N2H+. Among them, 157 ratios are observable and could be considered as candidate chemical clocks.

astro-ph.GA↗

arXiv2025

Creator-Side Recommender System: Challenges, Designs, and Applications

Users and creators are two crucial components of recommender systems. Typical recommender systems focus on the user side, providing the most suitable items based on each user's request. In such scenarios, a few items receive a majority of exposures, while many items receive very few. This imbalance leads to poorer experiences and decreased activity among the creators receiving less feedback, harming the recommender system in the long term. To this end, we develop a creator-side recommender system, called DualRec, to answer the following question: how to find the most suitable users for each item to enhance the creators' experience? We show that typical user-side recommendation algorithms, such as retrieval and ranking algorithms, can be adapted into the creator-side versions with just a few modifications. This greatly simplifies algorithm design in DualRec. Moreover, we discuss a unique challenge in DualRec: the user availability issue, which is not present in user-side recommender systems. To tackle this issue, we incorporate a user availability calculation (UAC) module to effectively enhance DualRec's performance. DualRec has already been implemented in Kwai, a short video recommendation system with over 100 millions user and over 10 million creators, significantly improving the experience for creators.

cs.IR↗

arXiv2025

Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes

In recent years, large amounts of electronic health records (EHRs) concerning chronic diseases have been collected to facilitate medical diagnosis. Modeling the dynamic properties of EHRs related to chronic diseases can be efficiently done using dynamic treatment regimes (DTRs). While reinforcement learning (RL) is a widely used method for creating DTRs, there is ongoing research in developing RL algorithms that can effectively handle large amounts of data. In this paper, we present a scalable kernel-based distributed Q-learning algorithm for generating DTRs. We perform both theoretical assessments and numerical analysis for the proposed approach. The results demonstrate that our algorithm significantly reduces the computational complexity associated with the state-of-the-art deep reinforcement learning methods, while maintaining comparable generalization performance in terms of accumulated rewards across stages, such as survival time or cumulative survival probability.

cs.LG↗

arXiv2025

Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach

We consider the problem of matrix completion with graphs as side information depicting the interrelations between variables. The key challenge lies in leveraging the similarity structure of the graph to enhance matrix recovery. Existing approaches, primarily based on graph Laplacian regularization, suffer from several limitations: (1) they focus only on the similarity between neighboring variables, while overlooking long-range correlations; (2) they are highly sensitive to false edges in the graphs and (3) they lack theoretical guarantees regarding statistical and computational complexities. To address these issues, we propose in this paper a novel graph regularized matrix completion algorithm called GSGD, based on preconditioned projected gradient descent approach. We demonstrate that GSGD effectively captures the higher-order correlation information behind the graphs, and achieves superior robustness and stability against the false edges. Theoretically, we prove that GSGD achieves linear convergence to the global optimum with near-optimal sample complexity, providing the first theoretical guarantees for both recovery accuracy and efficacy in the perspective of nonconvex optimization. Our numerical experiments on both synthetic and real-world data further validate that GSGD achieves superior recovery accuracy and scalability compared with several popular alternatives.

cs.LG↗

arXiv2025

DULRTC-RME: A Deep Unrolled Low-rank Tensor Completion Network for Radio Map Estimation

Radio maps enrich radio propagation and spectrum occupancy information, which provides fundamental support for the operation and optimization of wireless communication systems. Traditional radio maps are mainly achieved by extensive manual channel measurements, which is time-consuming and inefficient. To reduce the complexity of channel measurements, radio map estimation (RME) through novel artificial intelligence techniques has emerged to attain higher resolution radio maps from sparse measurements or few observations. However, black box problems and strong dependency on training data make learning-based methods less explainable, while model-based methods offer strong theoretical grounding but perform inferior to the learning-based methods. In this paper, we develop a deep unrolled low-rank tensor completion network (DULRTC-RME) for radio map estimation, which integrates theoretical interpretability and learning ability by unrolling the tedious low-rank tensor completion optimization into a deep network. It is the first time that algorithm unrolling technology has been used in the RME field. Experimental results demonstrate that DULRTC-RME outperforms existing RME methods.

eess.SP↗

arXiv2025

Sampling Electronic Fock States using Determinant Quantum Monte Carlo

Analog quantum simulation based on ultracold atoms in optical lattices has catalyzed significant breakthroughs in the study of quantum many-body systems. These simulations rely on the statistical sampling of electronic Fock states, which are not easily accessible in classical algorithms. In this work, we modify the determinant quantum Monte Carlo by integrating a Fock-state update mechanism alongside the auxiliary field. This method enables efficient sampling of Fock-state configurations. The Fock-state restrictive sampling scheme further enables the pre-selection of multiple ensembles at no additional computational cost, thereby broadening the scope of simulation to more general systems and models. Employing this method, we analyze static correlations of the Hubbard model up to the fourth order and achieve quantitative agreement with cold-atom experiments. The simulations of dynamical spectroscopies of the Hubbard and Kondo-lattice models further demonstrate the reliability and advantage of this method.

cond-mat.quant-gas↗

arXiv2025

Chartist: Task-driven Eye Movement Control for Chart Reading

To design data visualizations that are easy to comprehend, we need to understand how people with different interests read them. Computational models of predicting scanpaths on charts could complement empirical studies by offering estimates of user performance inexpensively; however, previous models have been limited to gaze patterns and overlooked the effects of tasks. Here, we contribute Chartist, a computational model that simulates how users move their eyes to extract information from the chart in order to perform analysis tasks, including value retrieval, filtering, and finding extremes. The novel contribution lies in a two-level hierarchical control architecture. At the high level, the model uses LLMs to comprehend the information gained so far and applies this representation to select a goal for the lower-level controllers, which, in turn, move the eyes in accordance with a sampling policy learned via reinforcement learning. The model is capable of predicting human-like task-driven scanpaths across various tasks. It can be applied in fields such as explainable AI, visualization design evaluation, and optimization. While it displays limitations in terms of generalizability and accuracy, it takes modeling in a promising direction, toward understanding human behaviors in interacting with charts.

cs.HC↗

arXiv2025

A Unified Regularization Approach to High-Dimensional Generalized Tensor Bandits

Modern decision-making scenarios often involve data that is both high-dimensional and rich in higher-order contextual information, where existing bandits algorithms fail to generate effective policies. In response, we propose in this paper a generalized linear tensor bandits algorithm designed to tackle these challenges by incorporating low-dimensional tensor structures, and further derive a unified analytical framework of the proposed algorithm. Specifically, our framework introduces a convex optimization approach with the weakly decomposable regularizers, enabling it to not only achieve better results based on the tensor low-rankness structure assumption but also extend to cases involving other low-dimensional structures such as slice sparsity and low-rankness. The theoretical analysis shows that, compared to existing low-rankness tensor result, our framework not only provides better bounds but also has a broader applicability. Notably, in the special case of degenerating to low-rank matrices, our bounds still offer advantages in certain scenarios.

cs.LG↗