arXiv subjects

Wenbo Sun

Publications and source records attributed to Wenbo Sun.

At least 37 records · Page 2Linked to original sources

arXiv2025

Resolving the joint ergodicity problem for Hardy sequences

The joint ergodicity classification problem aims to characterize those sequences which are jointly ergodic along an arbitrary dynamical system if and only if they satisfy two natural, simpler-to-verify conditions on this system. These two conditions, dubbed the difference and product ergodicity conditions, naturally arise from Berend and Bergelson's pioneering work on joint ergodicity. Elaborating on our earlier work, we investigate this problem for Hardy sequences of polynomial growth, this time without making any independence assumptions on the sequences. Our main result establishes the "difficult" direction of the problem: if a Hardy family satisfies the difference and product ergodicity conditions on a given system, then it is jointly ergodic for this system. We also find that, surprisingly, the converse fails for certain pathological families of Hardy sequences, even though it holds for all "reasonable" Hardy families. We conclude by suggesting potential fixes to the statement of this problem. New ideas of independent interest developed in this paper include the structure theory of a family of factors generalizing Host-Kra and box factors; a strengthening of Tao-Ziegler's concatenation results; and the most robust extension of a seminorm smoothing argument.

math.DS↗

arXiv2025

Superconducting Coherence Peak in Near-Field Radiative Heat Transfer

Enhancement and peaks in near-field radiative heat transfer (NFRHT) typically arise due to surface phonon-polaritons, plasmon-polaritons, and electromagnetic (EM) modes in structured materials. However, the role of material quantum coherence in enhancing near-field radiative heat transfer remains unexplored. Here, we unravel that NFRHT in superconductor-ferromagnetic systems displays a unique peak at the superconducting phase transition that originates from the quantum coherence of Bogoliubov quasiparticles in superconductors. Our theory takes into account evanescent EM radiation emanating from fluctuating currents related to Cooper pairs and Bogoliubov quasiparticles in stark contrast to the current-current correlations induced by free electrons in conventional materials. Our proposed NFRHT configuration exploits ferromagnetic resonance at frequencies deep inside the superconducting band gap to isolate this superconducting coherence peak. Furthermore, we reveal that Cooper pairs and Bogoliubov quasiparticles have opposite effects on near-field thermal radiation and isolate their effects on many-body radiative heat transfer near superconductors. Our proposed phenomenon can have applications for developing thermal isolators and heat sinks in superconducting circuits.

cond-mat.mes-hall↗

arXiv2025

Uncertainty-aware Efficient Subgraph Isomorphism using Graph Topology

Subgraph isomorphism, also known as subgraph matching, is typically regarded as an NP-complete problem. This complexity is further compounded in practical applications where edge weights are real-valued and may be affected by measurement noise and potential missing data. Such graph matching routinely arises in applications such as image matching and map matching. Most subgraph matching methods fail to perform node-to-node matching under presence of such corruptions. We propose a method for identifying the node correspondence between a subgraph and a full graph in the inexact case without node labels in two steps - (a) extract the minimal unique topology preserving subset from the subgraph and find its feasible matching in the full graph, and (b) implement a consensus-based algorithm to expand the matched node set by pairing unique paths based on boundary commutativity. To demonstrate the effectiveness of the proposed method, a simulation is performed on the Erdos-Renyi random graphs and two case studies are performed on the image-based affine covariant features dataset and KITTI stereo dataset respectively. Going beyond the existing subgraph matching approaches, the proposed method is shown to have realistically sub-linear computational efficiency, robustness to random measurement noise, and good statistical properties. Our method is also readily applicable to the exact matching case without loss of generality.

stat.ML↗

arXiv2025

ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding

Continual Knowledge Graph Embedding (CKGE) seeks to integrate new knowledge while preserving past information. However, existing methods struggle with efficiency and scalability due to two key limitations: (1) suboptimal knowledge preservation between snapshots caused by manually designed node/relation importance scores that ignore graph dependencies relevant to the downstream task, and (2) computationally expensive graph traversal for node/relation importance calculation, leading to slow training and high memory overhead. To address these limitations, we introduce ETT-CKGE (Efficient, Task-driven, Tokens for Continual Knowledge Graph Embedding), a novel task-guided CKGE method that leverages efficient task-driven tokens for efficient and effective knowledge transfer between snapshots. Our method introduces a set of learnable tokens that directly capture task-relevant signals, eliminating the need for explicit node scoring or traversal. These tokens serve as consistent and reusable guidance across snapshots, enabling efficient token-masked embedding alignment between snapshots. Importantly, knowledge transfer is achieved through simple matrix operations, significantly reducing training time and memory usage. Extensive experiments across six benchmark datasets demonstrate that ETT-CKGE consistently achieves superior or competitive predictive performance, while substantially improving training efficiency and scalability compared to state-of-the-art CKGE methods. The code is available at: https://github.com/lijingzhu1/ETT-CKGE/tree/main

cs.CL↗

arXiv2025

Nanophotonic Super-dephasing in Collective Atom-Atom Interactions

Pure dephasing and spontaneous emission are two non-unitary processes of atoms or spins interacting with fluctuating electromagnetic (EM) modes. The dissipative collective emission processes (e.g., superradiance) originate from interactions with EM modes in resonance with atoms and have received considerable attention. Meanwhile, the analogous non-dissipative collective dephasing phenomena mediated by EM environments remain poorly understood. Here, we introduce the nano-EM super-dephasing phenomenon arising in the photonic environments near materials. We show that collective dephasing in this nano-EM environment is enhanced by over 10 orders of magnitude compared to free space or cavities. This giant enhancement originates from long-range correlations in off-resonant, low-frequency evanescent EM fluctuations, which lead to collectively accelerated (super-) or suppressed (sub-) dephasing in many-body entangled states. We further unravel that nano-EM collective dephasing exhibits universal interaction ranges near materials with different anisotropy that can be reciprocal or non-reciprocal. This nano-EM interaction range, which is not present in free-space and cavities, leads to unique scaling laws of super-dephasing in GHZ states different from the conventional $N^2$ scaling of superradiance. Finally, we discuss how to experimentally isolate and control super-dephasing to open interesting frontiers for scalable quantum systems.

quant-ph↗

arXiv2025

Ilargi: a GPU Compatible Factorized ML Model Training Framework

The machine learning (ML) training over disparate data sources traditionally involves materialization, which can impose substantial time and space overhead due to data movement and replication. Factorized learning, which leverages direct computation on disparate sources through linear algebra (LA) rewriting, has emerged as a viable alternative to improve computational efficiency. However, the adaptation of factorized learning to leverage the full capabilities of modern LA-friendly hardware like GPUs has been limited, often requiring manual intervention for algorithm compatibility. This paper introduces Ilargi, a novel factorized learning framework that utilizes matrix-represented data integration (DI) metadata to facilitate automatic factorization across CPU and GPU environments without the need for costly relational joins. Ilargi incorporates an ML-based cost estimator to intelligently selects between factorization and materialization based on data properties, algorithm complexity, hardware environments, and their interactions. This strategy ensures up to 8.9x speedups on GPUs and achieves over 20% acceleration in batch ML training workloads, thereby enhancing the practicability of ML training across diverse data integration scenarios and hardware platforms. To our knowledge, this work is the very first effort in GPU-compatible factorized learning.

cs.LG↗

arXiv2025

KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion

While deep-learning-enabled recommender systems demonstrate strong performance benchmarks, many struggle to adapt effectively in real-world environments due to limited use of user-item relationship data and insufficient transparency in recommendation generation. Traditional collaborative filtering approaches fail to integrate multifaceted item attributes, and although Factorization Machines account for item-specific details, they overlook broader relational patterns. Collaborative knowledge graph-based models have progressed by embedding user-item interactions with item-attribute relationships, offering a holistic perspective on interconnected entities. However, these models frequently aggregate attribute and interaction data in an implicit manner, leaving valuable relational nuances underutilized. This study introduces the Knowledge Graph Attention Network with Information Fusion (KGIF), a specialized framework designed to merge entity and relation embeddings explicitly through a tailored self-attention mechanism. The KGIF framework integrates reparameterization via dynamic projection vectors, enabling embeddings to adaptively represent intricate relationships within knowledge graphs. This explicit fusion enhances the interplay between user-item interactions and item-attribute relationships, providing a nuanced balance between user-centric and item-centric representations. An attentive propagation mechanism further optimizes knowledge graph embeddings, capturing multi-layered interaction patterns. The contributions of this work include an innovative method for explicit information fusion, improved robustness for sparse knowledge graphs, and the ability to generate explainable recommendations through interpretable path visualization.

cs.LG↗

arXiv2024

Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes

Deep neural networks (DNNs) are often constructed under the closed-world assumption, which may fail to generalize to the out-of-distribution (OOD) data. This leads to DNNs producing overconfident wrong predictions and can result in disastrous consequences in safety-critical applications. Existing OOD detection methods mainly rely on curating a set of OOD data for model training or hyper-parameter tuning to distinguish OOD data from training data (also known as in-distribution data or InD data). However, OOD samples are not always available during the training phase in real-world applications, hindering the OOD detection accuracy. To overcome this limitation, we propose a Gaussian-process-based OOD detection method to establish a decision boundary based on InD data only. The basic idea is to perform uncertainty quantification of the unconstrained softmax scores of a DNN via a multi-class Gaussian process (GP), and then define a score function to separate InD and potential OOD data based on their fundamental differences in the posterior predictive distribution from the GP. Two case studies on conventional image classification datasets and real-world image datasets are conducted to demonstrate that the proposed method outperforms the state-of-the-art OOD detection methods when OOD samples are not observed in the training phase.

stat.ML↗

arXiv2024

Computational Electromagnetics Meets Spin Qubits: Controlling Noise Effects in Quantum Sensing and Computing

Solid-state spin qubits have emerged as promising platforms for quantum information. Despite extensive efforts in controlling noise in spin qubit quantum applications, one important but less controlled noise source is near-field electromagnetic fluctuations. Low-frequency (MHz and GHz) electromagnetic fluctuations are significantly enhanced near lossy material components in quantum applications, including metallic/superconducting gates necessary for controlling spin qubits in quantum computing devices and materials/nanostructures to be probed in quantum sensing. Although controlling this low-frequency electromagnetic fluctuation noise is crucial for improving the performance of quantum devices, current efforts are hindered by computational challenges. In this paper, we leverage advanced computational electromagnetics techniques, especially fast and accurate volume integral equation based solvers, to overcome the computational obstacle. We introduce a quantum computational electromagnetics framework to control low-frequency magnetic fluctuation noise and enhance spin qubit device performance. Our framework extends the application of computational electromagnetics to spin qubit quantum devices. Furthermore, we demonstrate the application of our framework in realistic quantum devices. Our work paves the way for device engineering to control magnetic fluctuations and improve the performance of spin qubit quantum sensing and computing.

quant-ph↗

arXiv2024

Spherical higher order Fourier analysis over finite fields III: a spherical Gowers inverse theorem

This paper is the third part of the series "Spherical higher order Fourier analysis over finite fields", aiming to develop the higher order Fourier analysis method along spheres over finite fields, and to solve the geometric Ramsey conjecture in the finite field setting. In this paper, we prove an inverse theorem over finite field for spherical Gowers norms, i.e. a local Gowers norm supported on a sphere. We show that if the $(s+1)$-th spherical Gowers norm of a 1-bounded function $f\colon\mathbb{F}_{p}^{d}\to \mathbb{C}$ is at least $ε$ and if $d$ is sufficiently large depending only on $s$, then $f$ correlates on the sphere with a $p$-periodic $s$-step nilsequence, where the bounds for the complexity and correlation depend only on $d$ and $ε$. This result will be used in later parts of the series to prove the geometric Ramsey conjecture in the finite field setting.

math.NT↗

arXiv2024

Spherical higher order Fourier analysis over finite fields IV: an application to the Geometric Ramsey Conjecture

This paper is the fourth and the last part of the series "Spherical higher order Fourier analysis over finite fields", aiming to develop the higher order Fourier analysis method along spheres over finite fields, and to solve the Geometric Ramsey Conjecture in the finite field setting. In this paper, we proof a conjecture of Graham on the Remsey properties for spherical configurations in the finite field setting. To be more precise, we show that for any spherical configuration $X$ of $\mathbb{F}_{p}^{d}$ of complexity at most $C$ with $d$ being sufficiently large with respect to $C$ and $\vert X\vert$, and for some prime $p$ being sufficiently large with respect to $C$, $\vert X\vert$ and $ε>0$, any set $E\subseteq \mathbb{F}_{p}^{d}$ with $\vert E\vert>εp^{d}$ contains at least $\gg_{C,ε,\vert X\vert}p^{(k+1)d-(k+1)k/2}$ congruent copies of $X$, where $k$ is the dimension of $\text{span}_{\mathbb{F}_{p}}(X-X)$. The novelty of our approach is that we avoid the use of harmonic analysis, and replace it by the theory of spherical higher order Fourier analysis developed in previous parts of the series.

math.NT↗

arXiv2024

Spherical higher order Fourier analysis over finite fields II: additive combinatorics for shifted ideals

This paper is the second part of the series "Spherical higher order Fourier analysis over finite fields", aiming to develop the higher order Fourier analysis method along spheres over finite fields, and to solve the geometric Ramsey conjecture in the finite field setting. In this paper, we study additive combinatorial properties for shifted modules, i.e. the structure of sets of the form $E\hat{+} E$, where $E$ is a collection of shifted modules of the polynomial ring $\mathbb{R}[x_{1},\dots,x_{d}]$ and we identify two modules if their difference contains the zero polynomial. We show that under appropriate definitions, the set $E\hat{+} E$ enjoys properties similar to the conventional setting where $E$ is a subset of an abelian group. In particular, among other results, we prove the Balog-Gowers-Szemerédi theorem, the Rusza's quasi triangle inequality and a weak form of the Plünnecke-Rusza theorem in the setting of shifted modules. We also show that for a special class of maps $ξ$ from $\mathbb{Z}_{K}^{d}$ to the collection of all shifted modules of $\mathbb{R}[x_{1},\dots,x_{d}]$, if the set $ξ(\mathbb{Z}_{K}^{d})+ξ(\mathbb{Z}_{K}^{d})$ has large additive energy, then $ξ$ is an almost linear Freiman homomorphism. This result is the crucial additive combinatorial input we need to prove the spherical Gowers inverse theorem in later parts of the series.

math.AC↗

arXiv2024

Spherical higher order Fourier analysis over finite fields I: equidistribution for nilsequences

This paper is the first part of the series "Spherical higher order Fourier analysis over finite fields", aiming to develop the higher order Fourier analysis method along spheres over finite fields, and to solve the geometric Ramsey conjecture in the finite field setting. In this paper, we prove a quantitative equidistribution theorem for polynomial sequences in a nilmanifold, where the average is taken along spheres instead of cubes. To be more precise, let $Ω\subseteq \mathbb{Z}^{d}$ be the preimage of a sphere $\mathbb{F}_{p}^{d}$ under the natural embedding from $\mathbb{Z}^{d}$ to $\mathbb{F}_{p}^{d}$. We showed that if a rational polynomial sequence $(g(n)Γ)_{n\inΩ}$ is not equidistributed on a nilmanifold $G/Γ$, then there exists a nontrivial horizontal character $η$ of $G/Γ$ such that $η\circ g \mod \mathbb{Z}$ vanishes on $Ω$. We also prove quantitative equidistribution and factorization theorems for more general sets arising from quadratic forms defined over $\mathbb{F}_{p}^{d}$. These results will serve as fundamental tools in later parts of the series to proof the spherical Gowers inverse theorem and the geometric Ramsey conjecture.

math.NT↗

arXiv2024

Joint transitivity for linear iterates

We establish sufficient and necessary conditions for the joint transitivity of linear iterates in a minimal topological dynamical system with commuting transformations. This result provides the first topological analogue of the classical Berend and Bergelson joint ergodicity criterion in measure-preserving systems.

math.DS↗

arXiv2024

Averages of completely multiplicative functions over the Gaussian integers -- a dynamical approach

We prove a pointwise convergence result for additive ergodic averages associated with certain multiplicative actions of the Gaussian integers. We derive several applications in dynamics and number theory, including: (i) Wirsing's theorem for Gaussian integers: if $f\colon \mathbb{G} \to \mathbb{R}$ is a bounded completely multiplicative function, then the following limit exists: $$\lim_{N \to \infty} \frac{1}{N^2} \sum_{1 \leq m, n \leq N} f(m + {\rm i} n).$$ (ii) An answer to a special case of a question of Frantzikinakis and Host: for any completely multiplicative real-valued function $f: \mathbb{N} \to \mathbb{R}$, the following limit exists: $$\lim_{N \to \infty} \frac{1}{N^2} \sum_{1 \leq m, n \leq N} f(m^2 + n^2).$$ (iii) A variant of a theorem of Bergelson and Richter on ergodic averages along the $Ω$ function: if $(X,T)$ is a uniquely ergodic system with unique invariant measure $μ$, then for any $x\in X$ and $f\in C(X)$, $$\lim_{N\to\infty}\frac{1}{N^2}\sum_{1 \leq m, n \leq N} f(T^{Ω(m^2 + n^2)}x)=\int_Xf \ dμ.$$

math.DS↗

arXiv2024

Accelerating Machine Learning Queries with Linear Algebra Query Processing

The rapid growth of large-scale machine learning (ML) models has led numerous commercial companies to utilize ML models for generating predictive results to help business decision-making. As two primary components in traditional predictive pipelines, data processing, and model predictions often operate in separate execution environments, leading to redundant engineering and computations. Additionally, the diverging mathematical foundations of data processing and machine learning hinder cross-optimizations by combining these two components, thereby overlooking potential opportunities to expedite predictive pipelines. In this paper, we propose an operator fusing method based on GPU-accelerated linear algebraic evaluation of relational queries. Our method leverages linear algebra computation properties to merge operators in machine learning predictions and data processing, significantly accelerating predictive pipelines by up to 317x. We perform a complexity analysis to deliver quantitative insights into the advantages of operator fusion, considering various data and model dimensions. Furthermore, we extensively evaluate matrix multiplication query processing utilizing the widely-used Star Schema Benchmark. Through comprehensive evaluations, we demonstrate the effectiveness and potential of our approach in improving the efficiency of data processing and machine learning workloads on modern hardware.

cs.PF↗

arXiv2024

Giant Enhancement of Vacuum Friction in Spinning YIG Nanospheres

Experimental observations of vacuum radiation and vacuum frictional torque are challenging due to their vanishingly small effects in practical systems. For example, a rotating nanosphere in free space slows down due to friction from vacuum fluctuations with a stopping time around the age of the universe. Here, we show that a spinning yttrium iron garnet (YIG) nanosphere near aluminum or YIG slabs exhibits vacuum radiation eight orders of magnitude larger than other metallic or dielectric spinning nanospheres. We achieve this giant enhancement by exploiting the large near-field magnetic local density of states in YIG systems, which occurs in the low-frequency GHz regime comparable to the rotation frequency. Furthermore, we propose a realistic experimental setup for observing the effects of this large vacuum radiation and frictional torque under experimentally accessible conditions.

quant-ph↗

arXiv2023

Automatic Identification of Driving Maneuver Patterns using a Robust Hidden Semi-Markov Models

There is an increase in interest to model driving maneuver patterns via the automatic unsupervised clustering of naturalistic sequential kinematic driving data. The patterns learned are often used in transportation research areas such as eco-driving, road safety, and intelligent vehicles. One such model capable of modeling these patterns is the Hierarchical Dirichlet Process Hidden Semi-Markov Model (HDP-HSMM), as it is often used to estimate data segmentation, state duration, and transition probabilities. While this model is a powerful tool for automatically clustering observed sequential data, the existing HDP-HSMM estimation suffers from an inherent tendency to overestimate the number of states. This can result in poor estimation, which can potentially impact impact transportation research through incorrect inference of driving patterns. In this paper, a new robust HDP-HSMM (rHDP-HSMM) method is proposed to reduce the number of redundant states and improve the consistency of the model's estimation. Both a simulation study and a case study using naturalistic driving data are presented to demonstrate the effectiveness of the proposed rHDP-HSMM in identifying and inference of driving maneuver patterns.

stat.ML↗