arXiv subjects

Bo Fu

Publications and source records attributed to Bo Fu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Dirac Fermions and Topological Phases in Magnetic Topological Insulator Films

We develop a Dirac fermion theory for topological phases in magnetic topological insulator films. The theory is based on exact solutions of the energies and the wave functions for an effective model of the three-dimensional topological insulator (TI) film. It is found that the TI film consists of a pair of massless or massive Dirac fermions for the surface states, and a series of massive Dirac fermions for the bulk states. The massive Dirac fermion always carries zero or integer quantum Hall conductance when the valence band is fully occupied while the massless Dirac fermion carries a one-half quantum Hall conductance when the chemical potential is located around the Dirac point for a finite range. The magnetic exchange interaction in the magnetic layers in the film can be used to manipulate either the masses or chirality of the Dirac fermions and gives rise to distinct topological phases, which cover the known topological insulating phases, such as quantum anomalous Hall effect, quantum spin Hall effect and axion effect, and also the novel topological metallic phases, such as half quantized Hall effect, half quantum mirror Hall effect, and metallic quantum anomalous Hall effect.

cond-mat.mes-hall↗

arXiv2025

Reinterpretation of chiral anomaly on a lattice

The chiral anomaly is a quantum mechanical effect for massless Dirac fermions in both particle physics and condensed matter physics. Here we present a set of effective models for single massless Dirac fermions in one- and three-dimensions in the whole Brillouin zone from higher-dimensional Chern insulators, which uniquely capture both the chiral fermion behavior near the Dirac point and high-energy states at Brillouin zone boundaries. In the presence of electromagnetic fields, the chiral coefficient $C_{5}$ is found to be chemical-potential dependent in general, but quantized precisely at the Fermi surface where chiral symmetry is preserved. This result provides an alternative interpretation of chiral anomaly on a lattice: the anomaly is caused by the symmetry-broken states far below the Fermi surface, and protected by the local chiral symmetry. Our analysis here might provide a potential theoretical foundation for applying the concept of chiral anomaly in condensed matter physics.

cond-mat.mes-hall↗

arXiv2025

Half-Quantized Hall Metal and Marginal Metal in Disordered Magnetic Topological Insulators

A semimagnetic topological insulator -- a heterostructure combining a topological insulator with a ferromagnet -- exhibits a half-quantized Hall effect, characterized by a quantized Hall conductance of $\frac{1}{2}\frac{e^{2}}{h}$ (where $e$ is the elementary charge and $h$ is the Planck constant), which reinforces the established understanding of topological phenomena in condensed matter physics. However, its stability in realistic, disordered systems remains poorly understood. Here, we demonstrate the robustness of the half-quantized Hall effect in weakly disordered systems, stemming from a single gapless Dirac cone of fermions and coexisting with weak antilocalization due to the $π$ Berry phase that suppresses backscattering. Furthermore, we uncover a marginal metallic phase emerging between weak antilocalization and Anderson insulation -- a transition that defies conventional metal-insulator transitions by lacking an isolated critical point -- where both conductance and normalized localization length exhibit scale invariance, independent of system size. The half-quantized Hall metal and the marginal metallic phase challenge existing localization theories and provide insights into disorder-driven topological phase transitions in magnetic topological insulators, opening avenues for exploring quantum materials and next-generation electronic devices.

cond-mat.dis-nn↗

arXiv2025

Quantum Geometric Renormalization of the Hall Coefficient and Unconventional Hall Resistivity in ZrTe5

The anomalous Hall effect (AHE), conventionally associated with time-reversal symmetry breaking in ferromagnetic materials, has recently been observed in nonmagnetic topological materials, raising questions about its origin. We unravel the unconventional Hall response in the nonmagnetic Dirac material ZrTe5, known for its massive Dirac bands and unique electronic and transport properties. Using the Kubo-Streda formula within the Landau level framework, we explore the interplay of quantum effects induced by the magnetic field (B) and disorder across the semiclassical and quantum regimes. In the semiclassical regime, the Hall resistivity remains linear in the magnetic field, but the Hall coefficient will be renormalized by the quantum geometric effects and electron-hole coherence, especially at low carrier densities where the disorder scattering dominates. In quantum limit, the Hall conductivity exhibits an unsaturating 1/B scaling. As a result, the transverse conductivity dominates transport in the ultra-quantum limit, and the Hall resistivity crosses over from B to B^{-1} dependence as the system transitions from the semiclassical regime to the quantum limit. This work elucidates the mechanisms underlying the unconventional Hall effect in ZrTe5 and provides insights into the AHE in other nonmagnetic Dirac materials as well.

cond-mat.mes-hall↗

arXiv2025

Large Foundation Model for Ads Recommendation

Online advertising relies on accurate recommendation models, with recent advances using pre-trained large-scale foundation models (LFMs) to capture users' general interests across multiple scenarios and tasks. However, existing methods have critical limitations: they extract and transfer only user representations (URs), ignoring valuable item representations (IRs) and user-item cross representations (CRs); and they simply use a UR as a feature in downstream applications, which fails to bridge upstream-downstream gaps and overlooks more transfer granularities. In this paper, we propose LFM4Ads, an All-Representation Multi-Granularity transfer framework for ads recommendation. It first comprehensively transfers URs, IRs, and CRs, i.e., all available representations in the pre-trained foundation model. To effectively utilize the CRs, it identifies the optimal extraction layer and aggregates them into transferable coarse-grained forms. Furthermore, we enhance the transferability via multi-granularity mechanisms: non-linear adapters for feature-level transfer, an Isomorphic Interaction Module for module-level transfer, and Standalone Retrieval for model-level transfer. LFM4Ads has been successfully deployed in Tencent's industrial-scale advertising platform, processing tens of billions of daily samples while maintaining terabyte-scale model parameters with billions of sparse embedding keys across approximately two thousand features. Since its production deployment in Q4 2024, LFM4Ads has achieved 10+ successful production launches across various advertising scenarios, including primary ones like Weixin Moments and Channels. These launches achieve an overall GMV lift of 2.45% across the entire platform, translating to estimated annual revenue increases in the hundreds of millions of dollars.

cs.LG↗

arXiv2025

Remember Past, Anticipate Future: Learning Continual Multimodal Misinformation Detectors

Nowadays, misinformation articles, especially multimodal ones, are widely spread on social media platforms and cause serious negative effects. To control their propagation, Multimodal Misinformation Detection (MMD) becomes an active topic in the community to automatically identify misinformation. Previous MMD methods focus on supervising detectors by collecting offline data. However, in real-world scenarios, new events always continually emerge, making MMD models trained on offline data consistently outdated and ineffective. To address this issue, training MMD models under online data streams is an alternative, inducing an emerging task named continual MMD. Unfortunately, it is hindered by two major challenges. First, training on new data consistently decreases the detection performance on past data, named past knowledge forgetting. Second, the social environment constantly evolves over time, affecting the generalization on future data. To alleviate these challenges, we propose to remember past knowledge by isolating interference between event-specific parameters with a Dirichlet process-based mixture-of-expert structure, and anticipate future environmental distributions by learning a continuous-time dynamics model. Accordingly, we induce a new continual MMD method DAEDCMD. Extensive experiments demonstrate that DAEDCMD can consistently and significantly outperform the compared methods, including six MMD baselines and three continual learning methods.

cs.CL↗

arXiv2025

Ferroelectrically Switchable Half-Quantized Hall Effect

Integrating ferroelectricity, antiferromagnetism, and topological quantum transport within a single material is rare, but crucial for developing next-generation quantum devices. Here, we propose a multiferroic heterostructure consisting of an antiferromagnetic MnBi$_2$Te$_4$ bilayer and an Sb$_2$Te$_3$ film is able to harbor the half-quantized Hall (HQH) effect with a ferroelectrically switchable Hall conductivity of $e^2/2h$. We first show that, in the energetically stable configuration, the antiferromagnetic MnBi$_2$Te$_4$ bilayer opens a gap in the top surface bands of Sb$_2$Te$_3$ through proximity effect, while its bottom surface bands remain gapless; consequently, HQH conductivity of $e^2/2h$ can be sustained clockwise or counterclockwise depending on antiferromagnetic configuration of the MnBi$_2$Te$_4$. Remarkably, when applying interlayer sliding within the MnBi$_2$Te$_4$ bilayer, its electric polarization direction associated with parity-time reversal symmetry breaking is reversed, accompanied by a reversal of the HQH conductivity. The proposed approach offers a powerful route to control topological quantum transport in antiferromagnetic materials by ferroelectricity.

cond-mat.mes-hall↗

arXiv2025

Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) exhibit promising advancements across various tasks, yet they still encounter significant trustworthiness issues. Prior studies apply Split Conformal Prediction (SCP) in language modeling to construct prediction sets with statistical guarantees. However, these methods typically rely on internal model logits or are restricted to multiple-choice settings, which hampers their generalizability and adaptability in dynamic, open-ended environments. In this paper, we introduce TRON, a two-step framework for risk control and assessment, applicable to any MLLM that supports sampling in both open-ended and closed-ended scenarios. TRON comprises two main components: (1) a novel conformal score to sample response sets of minimum size, and (2) a nonconformity score to identify high-quality responses based on self-consistency theory, controlling the error rates by two specific risk levels. Furthermore, we investigate semantic redundancy in prediction sets within open-ended contexts for the first time, leading to a promising evaluation metric for MLLMs based on average set size. Our comprehensive experiments across four Video Question-Answering (VideoQA) datasets utilizing eight MLLMs show that TRON achieves desired error rates bounded by two user-specified risk levels. Additionally, deduplicated prediction sets maintain adaptiveness while being more efficient and stable for risk assessment under different risk levels.

cs.CL↗

arXiv2025

Robust estimation of optimal dynamic treatment regimes with nonignorable missing covariates

Estimating optimal dynamic treatment regimes (DTRs) using observational data is often challenged by nonignorable missing covariates arsing from informative monitoring of patients in clinical practice. To address nonignorable missingness of pseudo-outcomes induced by nonignorable missing covariates, a weighted Q-learning approach using parametric Q-function models and a semiparametric missingness propensity model has recently been proposed. However, misspecification of parametric Q-functions at later stages of a DTR can propagate estimation errors to earlier stages via the pseudo-outcomes themselves and indirectly through biased estimation of the missingness propensity of the pseudo-outcomes. This robustness concern motivates us to develop a direct-search-based optimal DTR estimator built on a robust and efficient value estimator, where nonparametric methods are employed for treatment propensity and Q-function estimation, and inverse probability weighting is applied using missingness propensity estimated with the aid of nonresponse instrumental variables. Specifically, in our value estimator, we replace weights estimated by prediction models of treatment propensity with stable weights estimated by balancing covariate functions in a reproducing-kernel Hilbert space (RKHS). Augmented by Q-functions estimated by RKHS-based smoothing splines, our value estimator mitigates the misspecification risk of the weighted Q-learning approach while maintaining the efficiency gain from employing pseudo-outcomes in missing data scenarios. The asymptotic properties of the proposed estimator are derived, and simulations demonstrate its superior performance over weighted Q-learning under model misspecification. We apply the proposed methods to investigate the optimal fluid strategy for sepsis patients using data from the MIMIC database.

stat.ME↗

arXiv2025

Integrating LLMs and Digital Twins for Adaptive Multi-Robot Task Allocation in Construction

Multi-robot systems are emerging as a promising solution to the growing demand for productivity, safety, and adaptability across industrial sectors. However, effectively coordinating multiple robots in dynamic and uncertain environments, such as construction sites, remains a challenge, particularly due to unpredictable factors like material delays, unexpected site conditions, and weather-induced disruptions. To address these challenges, this study proposes an adaptive task allocation framework that strategically leverages the synergistic potential of Digital Twins, Integer Programming (IP), and Large Language Models (LLMs). The multi-robot task allocation problem is formally defined and solved using an IP model that accounts for task dependencies, robot heterogeneity, scheduling constraints, and re-planning requirements. A mechanism for narrative-driven schedule adaptation is introduced, in which unstructured natural language inputs are interpreted by an LLM, and optimization constraints are autonomously updated, enabling human-in-the-loop flexibility without manual coding. A digital twin-based system has been developed to enable real-time synchronization between physical operations and their digital representations. This closed-loop feedback framework ensures that the system remains dynamic and responsive to ongoing changes on site. A case study demonstrates both the computational efficiency of the optimization algorithm and the reasoning performance of several LLMs, with top-performing models achieving over 97% accuracy in constraint and parameter extraction. The results confirm the practicality, adaptability, and cross-domain applicability of the proposed methods.

cs.RO↗

arXiv2025

Random-Flux-Induced Transition Sequence between Weak and Strong Topological Phases with Anisotropic Localization Properties

We demonstrate that random flux is able to drive nontrivial topological phase transitions, in particular between weak topological insulators (WTIs) and Chern insulators (CIs), illustrated on an anisotropic Wilson-Dirac model in two dimensions. Remarkably, an intriguing topological transition sequence WTIs$\rightarrow$CIs$\rightarrow$WTIs occurs with the reentrance to a WTI but of different weak topology, which is unattainable with chemical potential disorder. The involvement of anisotropy and weak topology in such a transition gives rise to emergent quasi-critical points, where eigen states are extended in one spatial direction but localized in the other one. This new quantum criticality lies outside the conventional quantum Hall universality class. We provide a comprehensive characterization of the random-flux-induced phase transitions and quantum criticality from both bulk and boundary perspectives. Our results describe a qualitatively new disorder effect based on the interplay of random flux with topological phases of matter.

cond-mat.mes-hall↗

arXiv2025

Digital Twin-based Smart Manufacturing: Dynamic Line Reconfiguration for Disturbance Handling

The increasing complexity of modern manufacturing, coupled with demand fluctuation, supply chain uncertainties, and product customization, underscores the need for manufacturing systems that can flexibly update their configurations and swiftly adapt to disturbances. However, current research falls short in providing a holistic reconfigurable manufacturing framework that seamlessly monitors system disturbances, optimizes alternative line configurations based on machine capabilities, and automates simulation evaluation for swift adaptations. This paper presents a dynamic manufacturing line reconfiguration framework to handle disturbances that result in operation time changes. The framework incorporates a system process digital twin for monitoring disturbances and triggering reconfigurations, a capability-based ontology model capturing available agent and resource options, a configuration optimizer generating optimal line configurations, and a simulation generation program initializing simulation setups and evaluating line configurations at approximately 400x real-time speed. A case study of a battery production line has been conducted to evaluate the proposed framework. In two implemented disturbance scenarios, the framework successfully recovers system throughput with limited resources, preventing the 26% and 63% throughput drops that would have occurred without a reconfiguration plan. The reconfiguration optimizer efficiently finds optimal solutions, taking an average of 0.03 seconds to find a reconfiguration plan for a manufacturing line with 51 operations and 40 available agents across 8 agent types.

cs.MA↗

arXiv2025

Accelerating Randomized Algorithms for Low-Rank Matrix Approximation

Randomized algorithms are overwhelming methods for low-rank approximation that can alleviate the computational expenditure with great reliability compared to deterministic algorithms. A crucial thought is generating a standard Gaussian matrix $\mathbf{G}$ and subsequently obtaining the orthonormal basis of the range of $\mathbf{AG}$ for a given matrix $\mathbf{A}$. Recently, the \texttt{farPCA} algorithm offers a framework for randomized algorithms, but the dense Gaussian matrix remains computationally expensive. Motivated by this, we introduce the standardized Bernoulli, sparse sign, and sparse Gaussian matrices to replace the standard Gaussian matrix in \texttt{farPCA} for accelerating computation. These three matrices possess a low computational expenditure in matrix-matrix multiplication and converge in distribution to a standard Gaussian matrix when multiplied by an orthogonal matrix under a mild condition. Therefore, the three corresponding proposed algorithms can serve as a superior alternative to fast adaptive randomized PCA (\texttt{farPCA}). Finally, we leverage random matrix theory (RMT) to derive a tighter error bound for \texttt{farPCA} without shifted techniques. Additionally, we extend this improved error bound to the error analysis of our three fast algorithms, ensuring that the proposed methods deliver more accurate approximations for large-scale matrices. Numerical experiments validate that the three algorithms achieve asymptotically the same performance as \texttt{farPCA} but with lower costs, offering a more efficient approach to low-rank matrix approximation.

stat.CO↗

arXiv2025

MDE-Edit: Masked Dual-Editing for Multi-Object Image Editing via Diffusion Models

Multi-object editing aims to modify multiple objects or regions in complex scenes while preserving structural coherence. This task faces significant challenges in scenarios involving overlapping or interacting objects: (1) Inaccurate localization of target objects due to attention misalignment, leading to incomplete or misplaced edits; (2) Attribute-object mismatch, where color or texture changes fail to align with intended regions due to cross-attention leakage, creating semantic conflicts (\textit{e.g.}, color bleeding into non-target areas). Existing methods struggle with these challenges: approaches relying on global cross-attention mechanisms suffer from attention dilution and spatial interference between objects, while mask-based methods fail to bind attributes to geometrically accurate regions due to feature entanglement in multi-object scenarios. To address these limitations, we propose a training-free, inference-stage optimization approach that enables precise localized image manipulation in complex multi-object scenes, named MDE-Edit. MDE-Edit optimizes the noise latent feature in diffusion models via two key losses: Object Alignment Loss (OAL) aligns multi-layer cross-attention with segmentation masks for precise object positioning, and Color Consistency Loss (CCL) amplifies target attribute attention within masks while suppressing leakage to adjacent regions. This dual-loss design ensures localized and coherent multi-object edits. Extensive experiments demonstrate that MDE-Edit outperforms state-of-the-art methods in editing accuracy and visual quality, offering a robust solution for complex multi-object image manipulation tasks.

cs.CV↗

arXiv2025

Robust Misinformation Detection by Visiting Potential Commonsense Conflict

The development of Internet technology has led to an increased prevalence of misinformation, causing severe negative effects across diverse domains. To mitigate this challenge, Misinformation Detection (MD), aiming to detect online misinformation automatically, emerges as a rapidly growing research topic in the community. In this paper, we propose a novel plug-and-play augmentation method for the MD task, namely Misinformation Detection with Potential Commonsense Conflict (MD-PCC). We take inspiration from the prior studies indicating that fake articles are more likely to involve commonsense conflict. Accordingly, we construct commonsense expressions for articles, serving to express potential commonsense conflicts inferred by the difference between extracted commonsense triplet and golden ones inferred by the well-established commonsense reasoning tool COMET. These expressions are then specified for each article as augmentation. Any specific MD methods can be then trained on those commonsense-augmented articles. Besides, we also collect a novel commonsense-oriented dataset named CoMis, whose all fake articles are caused by commonsense conflict. We integrate MD-PCC with various existing MD backbones and compare them across both 4 public benchmark datasets and CoMis. Empirical results demonstrate that MD-PCC can consistently outperform the existing MD baselines.

cs.CL↗

arXiv2025

A novel real-time aeroelastic hybrid simulation system of section model wind tunnel testing based on adaptive extended Kalman filter

Elastically-supported section model tests are the most basic experimental technique in wind engineering, where helical springs are commonly employed to simulate the two-degree-of-freedom low-order modal motions of flexible structures. However, the traditional technique has intrinsic limitations in accurately modeling nonlinear structural behaviors and accurate adjustments of nonlinear structural damping. This study proposes a novel Real-Time Aeroelastic Hybrid Simulation system for section model wind tunnel tests by integrating an active control algorithm of adaptive Kalman filter. The proposed system enables the simulation of nonlinear heave-transverse-torsion coupled vibrations of a section model under the action of the oncoming wind. The structural properties, i.g. mass, damping and stiffness, are numerically simulated via an active control system, and the aerodynamic forces are physically modelled via the model-wind interaction in the wind tunnel. To validate the feasibility and accuracy of the proposed RTAHS system, a MATLAB/Simulink-FLUENT/UDF co-simulation framework is developed. Numerical verification results indicate that the proposed algorithm effectively estimates the motion responses in both linear and nonlinear scenarios.

eess.SY↗

arXiv2025

Griffin Plots of vortex-induced vibrations: revealing self-similarity for quick estimation from transient displacement responses

Griffin plot relates the peak amplitudes of vortex-induced vibration to structrual mass-damping parameter, known as the Scruton number. Griffin plot serves as a fundamental tool in many engineering fields. This study confirms a general self-similarity in Griffin plots, where plots derived from transient responses at any Scruton number converge to a single, consisten curve. This self-similarity arises from weak aeroelastic nonlinearity in vortex-induced vibration, manifasting as amplitude-dependent aerodynamic damping. Based on this self-similarity property, we propose a numerical method to estimate Griffin plots from transient displacement responses at any Scruton number. The resulting plots align closely with experimental data for both cross-flow and torsional vortex-induced vibrations, highlighting robust self-similar behavior across different Scruton numbers. Furthermore, we observe a consistent trend in Griffin plots for a rectangular cylinder, closed-box, and double-girder bridge deck: the reciprocal of peak amplitudes shows an approximately linear relationship with the Scruton number, especially in torsional vortex-induced vibration. To generate this linearity, we develop a simple empirical model of vortex-induced forces. This model accurately reproduces the Griffin plot for a rectangular cylinder using aeroelastic parameters derived from a single Scruton number, significantly reducing the need for extensive experimental measurements.

physics.flu-dyn↗

arXiv2025

AMuLeT: Automated Design-Time Testing of Secure Speculation Countermeasures

In recent years, several hardware-based countermeasures proposed to mitigate Spectre attacks have been shown to be insecure. To enable the development of effective secure speculation countermeasures, we need easy-to-use tools that can automatically test their security guarantees early-on in the design phase to facilitate rapid prototyping. This paper develops AMuLeT, the first tool capable of testing secure speculation countermeasures for speculative leakage early in their design phase in simulators. Our key idea is to leverage model-based relational testing tools that can detect speculative leaks in commercial CPUs, and apply them to micro-architectural simulators to test secure speculation defenses. We identify and overcome several challenges, including designing an expressive yet realistic attacker observer model in a simulator, overcoming the slow simulation speed, and searching the vast micro-architectural state space for potential vulnerabilities. AMuLeT speeds up test throughput by more than 10x compared to a naive design and uses techniques to amplify vulnerabilities to uncover them within a limited test budget. Using AMuLeT, we launch for the first time, a systematic, large-scale testing campaign of four secure speculation countermeasures from 2018 to 2024--InvisiSpec, CleanupSpec, STT, and SpecLFB--and uncover 3 known and 6 unknown bugs and vulnerabilities, within 3 hours of testing. We also show for the first time that the open-source implementation of SpecLFB is insecure.

cs.CR↗