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Bo Fu

Publications and source records attributed to Bo Fu.

At least 19 recordsLinked to original sources
arXiv2026

Reachability in Five-Dimensional Vector Addition Systems Is PSPACE-Complete

Vector addition systems (VAS) are a fundamental model of concurrent computation in which transitions update nonnegative integer counters by adding integer vectors, provided all counters remain nonnegative. Closely related to Petri nets, VAS are the single-state counterpart of vector addition systems with states (VASS), which use finitely many states to restrict the available transitions. A classical simulation of $d$-VASS by $(d+3)$-VAS connects their reachability problems, but the complexity-theoretic picture of fixed-dimensional VAS remains largely unclear. While reachability in $2$-VASS is PSPACE-complete under binary encoding, the best known upper bound for $5$-VAS was only elementary. We prove that every reachable $5$-VAS instance has a reachability witness of length polynomial in its unary size. Consequently, $5$-VAS reachability is NL-complete under unary encoding and PSPACE-complete under binary encoding. We also establish a polynomial factorization of the reachability relation of every $d$-VAS with $d \ge 3$. For $5$-VAS, this strengthens Hopcroft and Pansiot's effective semilinearity of reachability sets to the full reachability relation. For every fixed $d\ge3$, it also yields an $\mathsf F_d$ upper bound for reachability in $(d+3)$-VAS.

cs.FL↗
arXiv2026

Bidirectional Cross-frequency Domain Interaction Network for UHD Low-Light Image Enhancement

Existing Ultra-High-Definition (UHD) low-light image enhancement (LLIE) methods typically rely on either spatial- or frequency-domain modeling, or independently process different frequency components for efficient restoration. However, they often fail to jointly exploit complementary spatial--frequency information and the inherent relationship between illumination and structural information across frequencies, leading to inconsistent illumination, residual noise, and detail artifacts. In this paper, we propose a novel Bidirectional Cross-frequency Domain Interaction Network (BiFreqNet) for UHD LLIE. Specifically, BiFreqNet incorporates a Dual-domain Feature Interaction (DFI) module into the multi-scale representations of a pretrained variational autoencoder (VAE) to perform reciprocal enhancement between low- and high-frequency features. DFI consists of a Spatial and Spectral-Aware Low-Frequency Enhancement (S$^2$LFE) block and a Cross-Frequency Enhancement (CFE) block. S$^2$LFE exploits high-frequency structural cues to guide low-frequency restoration and jointly models spatial and spectral information to recover global exposure and spatially varying illumination while suppressing sensor noise. CFE further leverages the enhanced low-frequency contextual information to guide adaptive high-frequency refinement, enabling faithful reconstruction of structures and fine details. Extensive experiments on four general and UHD LLIE benchmarks demonstrate that BiFreqNet achieves competitive or superior performance compared with state-of-the-art methods.

cs.CV↗
arXiv2026

Minimax and Adaptive Transfer Learning for Sparse Canonical Correlation Analysis

We develop a transfer learning framework for high-dimensional sparse canonical correlation analysis (CCA) with multiple heterogeneous source datasets. Our goal is to improve estimation of the target canonical coefficient matrices by borrowing information from related sources. We characterize source-target similarity through discrepancies in the joint covariance structure and establish minimax optimal rates under both joint and separate prediction losses, explicitly quantifying the gain from enlarged effective sample size and the cost of heterogeneity. To attain these rates, we first construct an oracle transfer estimator based on debiasing and truncated normalization. We then develop a computationally efficient procedure that adaptively achieves the minimax rate without prior knowledge of the sparsity levels, using aggregation to control transfer bias and a truncated normalization scheme to preserve transfer gains under the scale-sensitive prediction loss. A data-driven source detection procedure is further proposed to identify potentially informative auxiliary datasets. Our analysis also yields a generalized sin-theta perturbation theorem that removes a commonly imposed comparability condition on leading canonical correlations in the sparse CCA problem.

stat.ME↗
arXiv2026

Skill Sequence Planning for Collaborative Multi-Robot Construction

Robots have significant potential to automate construction processes. However, their industry adoption remains limited, partly because of the programming effort required to adapt robots to diverse tasks. This paper presents a skill sequence planning method that enables a heterogeneous team of multi-functional robots to collaboratively perform construction assembly work using reusable, preprogrammed skills such as grasping, drilling, and fastening. A central controller transforms the digital representation of the building into a construction relationship graph that represents construction entities, their states, and their parent-child relationships. Based on this representation, the system selects the next construction target, generates a symbolic sequence of skills for capable members of the robot team, and produces collision-free geometric motion plans for skill execution. The symbolic planning problem is dynamically regenerated as the construction state changes. An interactive digital twin presents the planned skill sequence and robot states to human co-workers for review and approval before execution. The method is evaluated through a construction assembly case study. By reducing the need to program robots separately for each task variation, the proposed approach supports more flexible deployment of collaborative robot teams in construction.

cs.RO↗
arXiv2026

Altermagnetism Induced Bogoliubov Fermi Surfaces Form Topological Superconductivity

We propose a novel type of topological superconductivity based on Bogoliubov Fermi surfaces (BFSs) in an altermagnetic topological insulator proximitized by an s-wave superconductor. The 3D altermagnetic topological insulator is characterized by zero-energy surface states in bulk nodal-ring phases and anisotropically shifted surface Dirac cones in topological insulating phases. The altermagnetic order in combination with superconductivity gives rise to highly anisotropic superconducting gaps with crystal-facet-dependent BFSs at the physical boundaries. These particular BFSs provide distinct platforms to realize topological superconductivity. We propose a quasi-1D nanowire in which the anisotropic BFSs experience topological phase transitions due to quantum confinement leading to Majorana zero modes (MZMs) at its ends. We further consider vortex phase transitions in the superconducting altermagnetic topological insulators. Remarkably, we find that the altermagnetic order allows us to transit between two distinct type of MZMs, one type is located at the vortex line, while the other type is located at the physical boundaries. Our work paves a new avenue utilizing altermagnetism-induced BFSs to engineer topological superconductivity through crystal anisotropy and quantum confinement.

cond-mat.supr-con↗
arXiv2026

Hidden topology and strong quantum metric bounds in trivial systems

The quantum metric integral (QMI) in two-dimensional (2D) systems is conventionally bounded from below by the Chern number. For systems with zero Chern number or identically vanishing Berry curvature, however, this bound becomes trivial and provides no useful geometric constraints. Here, we develop a dimension-reduction framework that decomposes the 2D QMI into lower-dimensional components in a nested-loop way. With this method, we establish a nonzero lower bound on the QMI arising from one-dimensional topological obstructions even when the conventional 2D topology is trivial. We explicitly demonstrate this mechanism in a tilted 2D Su-Schrieffer-Heeger model and an anisotropic Wilson-Dirac model with chiral symmetry. The resulting lower bounds of QMI are determined by the quantized Wannier bands along two different directions. We further investigate the quantum geometry in higher-order topological phases following the same strategy. By introducing Wannier-band basis obtained from the nested Wilson loop, we demonstrate that the Wannier-band QMI is bounded from below by the higher-order topological invariant, e.g. the quadrupole moment in Benalcazar-Bernevig-Hughes model. Our results establish nonzero lower bounds on QMI from a dimension-reduction framework, thereby generalizing the fundamental relation between quantum geometry and topology.

cond-mat.mes-hall↗
arXiv2026

First-Order Topological FFLO Transition and Superconducting Diode Sign Reversal in Altermagnetic Nanowires

Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state conventionally emerges via a second-order phase transition driven by finite magnetization. Here we show that a spin-orbit-coupled nanowire proximitized to $d$-wave altermagnets -- with zero net magnetization -- can realize topological FFLO states through a first-order transition, marked by a sharp sign-reversing superconducting diode effect. The altermagnetic field generates band-resolved competing pairing channels, giving rise to a double-valley free energy landscape whose global minimum switches discontinuously. It consequently leads to a first-order topological FFLO transition with simultaneous jumps in the Cooper pairing amplitude and finite center-of-mass momentum. Remarkably, this discontinuous topological reconfiguration substantially enhances the diode efficiency and drives a characteristic sharp sign reversal across the transition. The mechanism of such exotic phenomena is captured by Ginzburg--Landau theory. Our results provide a field-free altermagnetic route to topological FFLO states and identify their direct transport fingerprint.

cond-mat.supr-con↗
arXiv2026

3-VASS Reachability is in EXPSPACE

A VASS can be viewed as a finite-state automaton manipulating a fixed number (called its dimension) of counters holding non-negative values. The reachability problem, asking whether there is a run from one configuration, defined by a state and values of the counters, to another configuration, has been a long-standing algorithmic challenge in theoretical computer science. When the dimension is part of the input, the problem has been shown to be ACKERMANN-complete in 2021. For fixed dimension greater than 2, and in particular for dimension 3, the exact complexity of the reachability problem remains unclear. For a long time the known algorithms for the 3-dimensional VASS reachability problem had been non-elementary, while the best known lower bound is merely PSPACE hardness inherited from dimension 2. A recent breakthrough in (Czerwiński, Jecker, Lasota, Orlikowski, ICALP 2025) gave the first elementary upper bound for the problem, namely 2-EXPSPACE. In this paper it is shown that the reachability problem in 3-VASS belongs to EXPSPACE. The proof is based on a hierarchical pumpability analysis, yielding a doubly-exponential length bound on the shortest runs between two configurations.

cs.FL↗
arXiv2026

Invariant quantile regression for heterogeneous environments

In this paper, we propose an invariant quantile regression (IQR) framework specifically designed for multi-environment datasets, which captures the invariance across different environments. This framework is closely related to transfer learning, causal inference, and fair machine learning, and is motivated by scenarios in which the conditional probability of the response given covariates varies, while certain key variables remain invariant. This perspective differs notably from previous works that restrict attention to the conditional mean, which is often insufficient to capture the full causal relationships between covariates and the response in heterogeneous environments. In contrast, quantile-based invariance naturally accommodates heterogeneity, and aligns more closely with structural causal models, in which variables invariant across environments at one or multiple quantile levels directly indicate potential and stable causal variables. Moreover, we show that IQR may yield a larger set of endogenous variables compared to the conditional mean framework, which in turn promotes more effective exclusion of spurious (non-causal) variables. To achieve this, we introduce a Kernel-Smoothed Invariant Quantile Regression (KS-IQR) estimator, which leverages the underlying invariance structure and heterogeneity among environments, ensuring stable estimation across multiple environments. We establish the causal discovery properties of our method, demonstrate its ability to overcome the ``curse of endogeneity'', and derive an $\ell_2$ error bound for our estimator, all in a non-asymptotic framework. We apply our method to real data for causal discovery and obtain biologically meaningful relationships, recovering known signaling pathways and revealing additional quantile-specific effects.

stat.ME↗
arXiv2026

Suppression of Extrinsic Anomalous Hall Conductivity in Disordered Parity Anomalous Semimetal

We present an analytical investigation of the extrinsic contributions to the anomalous Hall conductivity in the context of the half-quantized Hall effect observed in disordered parity anomalous semimetal emerged from semi-magnetic topological insulator thin films. The gapless Dirac cone surface state, which embodies the quintessence of the half-quantized Hall effect, exhibits remarkable robustness against disorder scattering. Two primary extrinsic mechanisms, the side-jump and skew-scattering, are deemed irrelevant and make no contributions. These results establish the parity anomalous semimetal as a disorder-resilient quantum phase, thereby providing insights into Dirac fermion physics.

cond-mat.dis-nn↗
arXiv2026

STEDiff: Strengthening Text Embedding for Text-to-Image Alignment in Diffusion Model

Although pretrained text-to-image (T2I) generation models can produce high-quality images, they often fail to faithfully reflect the semantic intent of complex prompts due to stochastic noise and inherent model limitations. This issue frequently manifests as the model overlooking specific objects or failing to correctly bind attributes to their corresponding entities, a challenge referred to as semantic alignment. Unlike existing approaches that rely on computationally expensive fine-tuning or labor-intensive layout priors, we propose STEDiff, a training-free method designed to enhance semantic representations directly within the text-embedding space. Specifically, we introduce a method that primarily leverages the [EOT] token to strengthen the relevant semantics of sub-sentences and then replaces the corresponding tokens in the original prompt. Furthermore, a novel semantic enhancement loss is incorporated to enforce spatial constraints, ensuring that the semantics of each entity are precisely mapped to their respective image regions. Extensive quantitative and qualitative evaluations on the T2I-CompBench demonstrate that our method notably improves semantic consistency and generation integrity in complex scenarios.

cs.CV↗
arXiv2026

MiRD: Reliable Set-Valued Prediction for Open-Ended Question Answering via Miscoverage Risk Decomposition

Reliable set-valued prediction provides a principled way to mitigate hallucinations in open-ended question answering (QA), yet existing conformal approaches typically rely on a fragile premise: finite sampling must already produce at least one admissible candidate, or calibration examples violating this condition are discarded. In this paper, we introduce MiRD, a two-stage framework that decomposes overall miscoverage into sampling failure and conditional selection failure. In Stage I, MiRD establishes an expectation-level marginal upper bound on the probability that finite sampling produces no admissible answer under a fixed budget. In Stage II, conditioned on sampling success, MiRD calibrates a conformal selection threshold using admission-correlated nonconformity scores defined over the full calibration set, thereby preserving calibration-set integrity. Across three open-ended QA datasets and eight models, MiRD controls sampling risk, conditional selection risk, and overall miscoverage, while yielding tighter first-stage bounds than PAC-style alternatives and more adaptive prediction sets than successful-only calibration.

cs.CL↗
arXiv2026

Copula-enhanced Vision Transformer for high myopia diagnosis through OU UWF fundus images

The advancement of AI-assisted myopia screening necessitates the joint diagnosis of both-eye (OU) high myopia (HM) status and the prediction of axial length (AL). This clinical requirement introduces a complex mixed-type (binary-continuous) multitask learning task with bi-domain (OU) image covariates, giving rise to two key challenges: i) capture the inter-ocular asymmetry of OU images within a cutting-edge foundation model; ii) model and estimate the conditional dependence structure among mixed-type multivariate responses given image covariates. We address the challenges by: i) imposing residual adapters on the Vision Transformer foundation model to capture the OU similarity and heterogeneity simultaneously; ii) developing a four-dimensional copula loss that is implementable in PyTorch based on a latent variable expression for the Gaussian copula likelihood, and proposing a computationally efficient fast Monte Carlo Expectation Maximization (fMCEM) algorithm to estimate copula parameters. We further formulate a specific overfitting problem called stronger covariance phenomenon in multitask learning. We reveal the disturbance of the phenomenon to estimation of copula parameters and theoretically demonstrate the numerical stability of the proposed fMCEM algorithm against the disturbance. The application to our annotated OU ultra-widefield fundus image dataset and simulation on synthetic data demonstrate that our method stably enhances the predictive capabilities on both classification and regression tasks.

cs.CV↗
arXiv2026

Set-Valued Prediction for Large Language Models with Feasibility-Aware Coverage Guarantees

Large language models (LLMs) inherently operate over a large generation space, yet conventional usage typically reports the most likely generation (MLG) as a point prediction, which underestimates the model's capability: although the top-ranked response can be incorrect, valid answers may still exist within the broader output space and can potentially be discovered through repeated sampling. This observation motivates moving from point prediction to set-valued prediction, where the model produces a set of candidate responses rather than a single MLG. In this paper, we propose a principled framework for set-valued prediction, which provides feasibility-aware coverage guarantees. We show that, given the finite-sampling nature of LLM generation, coverage is not always achievable: even with multiple samplings, LLMs may fail to yield an acceptable response for certain questions within the sampled candidate set. To address this, we establish a minimum achievable risk level (MRL), below which statistical coverage guarantees cannot be satisfied. Building on this insight, we then develop a data-driven calibration procedure that constructs prediction sets from sampled responses by estimating a rigorous threshold, ensuring that the resulting set contains a correct answer with a desired probability whenever the target risk level is feasible. Extensive experiments on six language generation tasks with five LLMs demonstrate both the statistical validity and the predictive efficiency of our framework.

cs.CL↗
arXiv2026

Transfer learning for high-dimensional Factor-augmented sparse linear model

In this paper, we study transfer learning for high-dimensional factor-augmented sparse linear models, motivated by applications in economics and finance where strongly correlated predictors and latent factor structures pose major challenges for reliable estimation. Our framework simultaneously mitigates the impact of high correlation and removes the additional contributions of latent factors, thereby reducing potential model misspecification in conventional linear modeling. In such settings, the target dataset is often limited, but multiple heterogeneous auxiliary sources may provide additional information. We develop transfer learning procedures that effectively leverage these auxiliary datasets to improve estimation accuracy, and establish non-asymptotic $\ell_1$- and $\ell_2$-error bounds for the proposed estimators. To prevent negative transfer, we introduce a data-driven source detection algorithm capable of identifying informative auxiliary datasets and prove its consistency. In addition, we provide a hypothesis testing framework for assessing the adequacy of the factor model, together with a procedure for constructing simultaneous confidence intervals for the regression coefficients of interest. Numerical studies demonstrate that our methods achieve substantial gains in estimation accuracy and remain robust under heterogeneity across datasets. Overall, our framework offers a theoretical foundation and a practically scalable solution for incorporating heterogeneous auxiliary information in settings with highly correlated features and latent factor structures.

stat.ME↗
arXiv2026

Ferroelectrically Switchable Chirality in Topological Superconductivity

The interplay between ferroelectricity, magnetism, and superconductivity provides a rich platform for discovering novel quantum phenomena. Here, we develop an effective theory and propose a heterostructure composed of an antiferromagnetic bilayer MnBi$_{2}$Te$_{4}$ coupled with the s-wave superconductor Fe(Se,Te), enabling the realization of chiral topological superconductivity (CTSC) with switchable chirality. The chirality of the CTSC is controlled by the direction of spontaneous polarization, which arises from interlayer sliding-induced ferroelectricity or charge transfer in the bilayer MnBi$_{2}$Te$_{4}$. This sliding mechanism breaks the $\mathcal{M}_{z}\mathcal{T}$ and $\mathcal{PT}$ symmetries, leading to the anomalous Hall effect in the spin polarized metallic Dirac band and drives the emergence of CTSC when the s-wave superconductivity appears. Our work not only provides a new pathway to achieve and control topological superconductivity but also opens avenues for experimental exploration of Majorana physics and topological quantum computation.

cond-mat.supr-con↗
arXiv2026

Marginal Metals and Kosterlitz-Thouless Type Phase Transition in Disordered Altermagnets

Altermagnetism, a recently discovered magnetic phase characterized by spin-split bands without net magnetization, has emerged as promising platform for novel physics and potential applications. However, its stability against disorder-ubiquitous in real materials-remains poorly understood. Here, we study the electron localization properties of two-dimensional $d$-wave altermagnets subject to disorder. Remarkably, we discover a disorder-driven phase transition from a marginal metallic phase to an insulator, which falls into the Kosterlitz-Thouless class. We demonstrate this by strong numerical evidence and propose an interpretation in terms of vortex-antivortex pairs in the disorder-induced local in-plane spin magnetization. Moreover, we show that the characteristic spin anisotropy of altermagnets persists but gradually fades away across the transition. These changes directly affect the spin splitting features that are detectable in angle-resolved photoemission spectroscopy and tunneling magnetoconductance. Our findings provide a new perspective on recent experimental observations of altermagnetism in candidate materials.

cond-mat.mes-hall↗
arXiv2026

Symbolic Planning and Multi-Agent Path Finding in Extremely Dense Environments with Unassigned Agents

We introduce the Block Rearrangement Problem (BRaP), a challenging component of large warehouse management which involves rearranging storage blocks within dense grids to achieve a goal state. We formally define the BRaP as a graph search problem. Building on intuitions from sliding puzzle problems, we propose five search-based solution algorithms, leveraging joint configuration space search, classical planning, multi-agent pathfinding, and expert heuristics. We evaluate the five approaches empirically for plan quality and scalability. Despite the exponential relation between search space size and block number, our methods demonstrate efficiency in creating rearrangement plans for deeply buried blocks in up to 80x80 grids.

cs.AI↗