arXiv subjects

Haoran Fan

Publications and source records attributed to Haoran Fan.

10 recordsLinked to original sources

arXiv2026

Zero-Field Thermal Hall Effect in Insulator

Fourier's law dictates that heat flow is usually parallel to the applied temperature gradient. However, under a high magnetic field, heat flow carried by both electrons in conductors and phonons in insulators can be deflected, a phenomenon known as thermal Hall effect. Intriguingly, we observe at zero field a spontaneous thermal Hall effect in an antiferromagnetic insulator. Despite a vanishingly small uncompensated magnetization, the magnitude of this effect is surprisingly large, comparable to typical responses induced by several teslas of external field. This zero-field behavior indicates that charge-neutral heat carriers can be governed by an intrinsic effective field arising from the unique spin arrangement. Our discovery challenges the centuries-old preconception of heat conduction and open up new avenues for exploring non-trivial topological responses in quantum materials.

cond-mat.str-el↗

arXiv2026

SLICE: Semantic Latent Injection via Compartmentalized Embedding for Image Watermarking

Watermarking the initial noise of diffusion models has emerged as a promising approach for image provenance, but content-independent noise patterns can be forged via inversion and regeneration attacks. Recent semantic-aware watermarking methods improve robustness by conditioning verification on image semantics. However, their reliance on a single global semantic binding makes them vulnerable to localized but globally coherent semantic edits. To address this limitation and provide a trustworthy semantic-aware watermark, we propose $\underline{\textbf{S}}$emantic $\underline{\textbf{L}}$atent $\underline{\textbf{I}}$njection via $\underline{\textbf{C}}$ompartmentalized $\underline{\textbf{E}}$mbedding ($\textbf{SLICE}$). Our framework decouples image semantics into four semantic factors (subject, environment, action, and detail) and precisely anchors them to distinct regions in the initial Gaussian noise. This fine-grained semantic binding enables advanced watermark verification where semantic tampering is detectable and localizable. We theoretically justify why SLICE enables robust and reliable tamper localization and provides statistical guarantees on false-accept rates. Experimental results demonstrate that SLICE significantly outperforms existing baselines against advanced semantic-guided regeneration attacks, substantially reducing attack success while preserving image quality and semantic fidelity. Overall, SLICE offers a practical, training-free provenance solution that is both fine-grained in diagnosis and robust to realistic adversarial manipulations.

cs.CV↗

arXiv2025

Small but Mighty: Enhancing Time Series Forecasting with Lightweight LLMs

While LLMs have demonstrated remarkable potential in time series forecasting, their practical deployment remains constrained by excessive computational demands and memory footprints. Existing LLM-based approaches typically suffer from three critical limitations: Inefficient parameter utilization in handling numerical time series patterns; Modality misalignment between continuous temporal signals and discrete text embeddings; and Inflexibility for real-time expert knowledge integration. We present SMETimes, the first systematic investigation of sub-3B parameter SLMs for efficient and accurate time series forecasting. Our approach centers on three key innovations: A statistically-enhanced prompting mechanism that bridges numerical time series with textual semantics through descriptive statistical features; A adaptive fusion embedding architecture that aligns temporal patterns with language model token spaces through learnable parameters; And a dynamic mixture-of-experts framework enabled by SLMs' computational efficiency, adaptively combining base predictions with domain-specific models. Extensive evaluations across seven benchmark datasets demonstrate that our 3B-parameter SLM achieves state-of-the-art performance on five primary datasets while maintaining 3.8x faster training and 5.2x lower memory consumption compared to 7B-parameter LLM baselines. Notably, the proposed model exhibits better learning capabilities, achieving 12.3% lower MSE than conventional LLM. Ablation studies validate that our statistical prompting and cross-modal fusion modules respectively contribute 15.7% and 18.2% error reduction in long-horizon forecasting tasks. By redefining the efficiency-accuracy trade-off landscape, this work establishes SLMs as viable alternatives to resource-intensive LLMs for practical time series forecasting. Code and models are available at https://github.com/xiyan1234567/SMETimes.

cs.CL↗

arXiv2024

A Survey on Adversarial Machine Learning for Code Data: Realistic Threats, Countermeasures, and Interpretations

Code Language Models (CLMs) have achieved tremendous progress in source code understanding and generation, leading to a significant increase in research interests focused on applying CLMs to real-world software engineering tasks in recent years. However, in realistic scenarios, CLMs are exposed to potential malicious adversaries, bringing risks to the confidentiality, integrity, and availability of CLM systems. Despite these risks, a comprehensive analysis of the security vulnerabilities of CLMs in the extremely adversarial environment has been lacking. To close this research gap, we categorize existing attack techniques into three types based on the CIA triad: poisoning attacks (integrity \& availability infringement), evasion attacks (integrity infringement), and privacy attacks (confidentiality infringement). We have collected so far the most comprehensive (79) papers related to adversarial machine learning for CLM from the research fields of artificial intelligence, computer security, and software engineering. Our analysis covers each type of risk, examining threat model categorization, attack techniques, and countermeasures, while also introducing novel perspectives on eXplainable AI (XAI) and exploring the interconnections between different risks. Finally, we identify current challenges and future research opportunities. This study aims to provide a comprehensive roadmap for both researchers and practitioners and pave the way towards more reliable CLMs for practical applications.

cs.CR↗

arXiv2024

Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey

Building effective machine learning (ML) workflows to address complex tasks is a primary focus of the Automatic ML (AutoML) community and a critical step toward achieving artificial general intelligence (AGI). Recently, the integration of Large Language Models (LLMs) into ML workflows has shown great potential for automating and enhancing various stages of the ML pipeline. This survey provides a comprehensive and up-to-date review of recent advancements in using LLMs to construct and optimize ML workflows, focusing on key components encompassing data and feature engineering, model selection and hyperparameter optimization, and workflow evaluation. We discuss both the advantages and limitations of LLM-driven approaches, emphasizing their capacity to streamline and enhance ML workflow modeling process through language understanding, reasoning, interaction, and generation. Finally, we highlight open challenges and propose future research directions to advance the effective application of LLMs in ML workflows.

cs.LG↗

arXiv2024

MicroCam: Leveraging Smartphone Microscope Camera for Context-Aware Contact Surface Sensing

The primary focus of this research is the discreet and subtle everyday contact interactions between mobile phones and their surrounding surfaces. Such interactions are anticipated to facilitate mobile context awareness, encompassing aspects such as dispensing medication updates, intelligently switching modes (e.g., silent mode), or initiating commands (e.g., deactivating an alarm). We introduce MicroCam, a contact-based sensing system that employs smartphone IMU data to detect the routine state of phone placement and utilizes a built-in microscope camera to capture intricate surface details. In particular, a natural dataset is collected to acquire authentic surface textures in situ for training and testing. Moreover, we optimize the deep neural network component of the algorithm, based on continual learning, to accurately discriminate between object categories (e.g., tables) and material constituents (e.g., wood). Experimental results highlight the superior accuracy, robustness and generalization of the proposed method. Lastly, we conducted a comprehensive discussion centered on our prototype, encompassing topics such as system performance and potential applications and scenarios.

cs.HC↗

arXiv2024

Phonon Heat Transport and Anisotropic Tuning of Quantum Fluctuations in a Frustrated Honeycomb Magnet

Honeycomb cobalt oxides containing 3$\it{d}$ Co$^{2+}$ ions might realize frustrated magnetism and novel quantum phases. Among candidate materials, Na$_{3}$Co$_{2}$SbO$_{6}$ stands out for its distorted honeycomb lattice and significant in-plane anisotropy, motivating vector-field tuning inside the honeycomb plane. Here we use thermal transport down to the mK regime to study twin-free crystals of Na$_{3}$Co$_{2}$SbO$_{6}$ subject to in-plane vector fields. We find that the thermal conductivity $\kappa$ never exceeds the heat-transport capability of phonons, rendering its suppression primarily due to phonon scattering off magnetic excitations and/or domain boundaries. The system's field-driven quantum criticality manifests itself as an abundance of magnetic fluctuations hindering the heat transport, which further depends on the field direction in an intriguing manner.

cond-mat.str-el↗

arXiv2024

Pseudospin Polarization of Composite Fermions under Uniaxial Strain

A two dimensional system with extra degrees of freedom, such as spin and valley, is of great interest in the study of quantum phase transitions. The critical condition when a transition between different multicomponent fractional quantum Hall states appears is one of the very few junctions for many body problems between theoretical calculations and experiments. In this work, we present that uniaxial strain induces pseudospin transitions of composite fermions in a two-dimensional hole gas. Determined from transport behavior, strain along <111> effectively changes pseudospin energy levels. We deduce that diagonal strain dominates these variations. Our experiment provides a wedge for manipulating two dimensional interacting systems mechanically.

cond-mat.mes-hall↗

arXiv2023

DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation

Few-shot segmentation (FSS) aims to segment novel classes in a query image by using only a small number of supporting images from base classes. However, in cross-domain few-shot segmentation (CD-FSS), leveraging features from label-rich domains for resource-constrained domains poses challenges due to domain discrepancies. This work presents a Dynamically Adaptive Refine (DARNet) method that aims to balance generalization and specificity for CD-FSS. Our method includes the Channel Statistics Disruption (CSD) strategy, which perturbs feature channel statistics in the source domain, bolstering generalization to unknown target domains. Moreover, recognizing the variability across target domains, an Adaptive Refine Self-Matching (ARSM) method is also proposed to adjust the matching threshold and dynamically refine the prediction result with the self-matching method, enhancing accuracy. We also present a Test-Time Adaptation (TTA) method to refine the model's adaptability to diverse feature distributions. Our approach demonstrates superior performance against state-of-the-art methods in CD-FSS tasks.

cs.CV↗

arXiv2023

Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization

Quantized neural networks (QNNs) have received increasing attention in resource-constrained scenarios due to their exceptional generalizability. However, their robustness against realistic black-box adversarial attacks has not been extensively studied. In this scenario, adversarial transferability is pursued across QNNs with different quantization bitwidths, which particularly involve unknown architectures and defense methods. Previous studies claim that transferability is difficult to achieve across QNNs with different bitwidths on the condition that they share the same architecture. However, we discover that under different architectures, transferability can be largely improved by using a QNN quantized with an extremely low bitwidth as the substitute model. We further improve the attack transferability by proposing \textit{quantization aware attack} (QAA), which fine-tunes a QNN substitute model with a multiple-bitwidth training objective. In particular, we demonstrate that QAA addresses the two issues that are commonly known to hinder transferability: 1) quantization shifts and 2) gradient misalignments. Extensive experimental results validate the high transferability of the QAA to diverse target models. For instance, when adopting the ResNet-34 substitute model on ImageNet, QAA outperforms the current best attack in attacking standardly trained DNNs, adversarially trained DNNs, and QNNs with varied bitwidths by 4.3\% $\sim$ 20.9\%, 8.7\% $\sim$ 15.5\%, and 2.6\% $\sim$ 31.1\% (absolute), respectively. In addition, QAA is efficient since it only takes one epoch for fine-tuning. In the end, we empirically explain the effectiveness of QAA from the view of the loss landscape. Our code is available at https://github.com/yyl-github-1896/QAA/

cs.CR↗