arXiv subjects

Hui Han

Publications and source records attributed to Hui Han.

At least 19 recordsLinked to original sources

arXiv2026

Enhanced Curie temperature and room-temperature 50-nm skyrmions achieved in hexagonal ferromagnet Mn5Ge3+x synthesized via a high-pressure method

The development of new high-temperature ultrasmall-size skyrmion materials holds immense significance for the promising applications of topological spintronic devices. In this study, we demonstrate that a high-pressure synthesis technique can significantly elevate the Curie temperature of Mn5Ge3+x crystals, from 294 K to 350 K. This enhancement is attributed to the combined effects of lattice contraction and increased Ge content, the conclusion supported by Density Functional Theory calculations. Additionally, our real-space magnetic imaging reveals the stability of dipolar skyrmions with diameters of approximately 50 nm at room temperature. Our micromagnetic simulations closely replicate the diverse experimental topological magnetic textures observed. Furthermore, magnetotransport measurements indicate the potential for the electrical distinction between various topological magnetic textures in skyrmion-based devices. We also report deterministic manipulations on single dipolar skyrmions in confined nanostructures by using in-plane currents. The observation, electrical manipulation, and electrical detection of room-temperature ultrasmall topological magnetic textures underscore the potential of Mn5Ge3+x as a promising platform for spintronic device applications.

cond-mat.mtrl-sci↗

arXiv2026

A Novel Preprocessing-Driven Approach to Remaining Useful Life (RUL) Prediction Using Temporal Convolutional Networks (TCN)

Accurate prediction of Remaining Useful Life (RUL) in aero-engines is vital for predictive maintenance, improved operational reliability, and reduced lifecycle costs. While deep learning approaches have demonstrated strong potential in this area, most existing methods focus primarily on model architecture design and treat input features uniformly, often neglecting the influence of data preprocessing. In this work, we propose a novel preprocessing pipeline that enhances RUL prediction by improving data quality and temporal representation before model training. Our approach leverages complete temporal sequences and generates RUL estimates at each timestep, enabling the model to capture fine-grained degradation dynamics and deliver continuous prognostic insights throughout the engine's operational life. To validate the effectiveness of the proposed pipeline, we conduct experiments on the NASA C-MAPSS dataset. Comparative evaluations against a suite of state-of-the-art neural models including CNN, RNN, LSTM, DCNN, TCN, BiGRU-TSAM, AGCNN, and ATCN, demonstrate that our approach consistently achieves superior accuracy and robustness in aero-engine RUL prediction. These results highlight the critical role of preprocessing in maximizing the effectiveness of neural prognostic models.

cs.LG↗

arXiv2026

VRAG-DFD: Verifiable Retrieval-Augmentation for MLLM-based Deepfake Detection

In Deepfake Detection (DFD) tasks, researchers proposed two types of MLLM-based methods: complementary combination with small DFD detectors, or static forgery knowledge injection. The lack of professional forgery knowledge hinders the performance of these DFD-MLLMs. To solve this, we deeply considered two insightful issues: How to provide high-quality associated forgery knowledge for MLLMs? AND How to endow MLLMs with critical reasoning abilities given noisy reference information? Notably, we attempted to address above two questions with preliminary answers by leveraging the combination of Retrieval-Augmented Generation (RAG) and Reinforcement Learning (RL). Through RAG and RL techniques, we propose the VRAG-DFD framework with accurate dynamic forgery knowledge retrieval and powerful critical reasoning capabilities. Specifically, in terms of data, we constructed two datasets with RAG: Forensic Knowledge Database (FKD) for DFD knowledge annotation, and Forensic Chain-of-Thought Dataset (F-CoT), for critical CoT construction. In terms of model training, we adopt a three-stage training method (Alignment->SFT->GRPO) to gradually cultivate the critical reasoning ability of the MLLM. In terms of performance, VRAG-DFD achieved SOTA and competitive performance on DFD generalization testing.

cs.CV↗

arXiv2026

On the Codesign of Scientific Experiments and Industrial Systems

The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.

physics.ins-det↗

arXiv2025

Fresnel Magnetic Imaging of Ultrasmall Skyrmion Lattices

Magnetic skyrmions with ultrasmall nanometric dimensions hold significant promise for next-generation high-density spintronic devices. Direct real-space imaging of these topological spin textures is critical for elucidating their emergent properties at the nanoscale. Here, we present Lorentz transmission electron microscopy studies of nanometric skyrmion lattices in B20-structured Mn0.5Fe0.5Ge crystals using Fresnel mode. According to conventional chiral discrimination methods relying on static bright-dark contrast, we demonstrate an abnormal periodic chiral-reversal phenomenon retrieved through the transport of intensity equation analysis of defocus-dependent Fresnel images. Through systematic off-axis electron holography experiments and numerical simulations, we attribute these chiral misinterpretations to the sinusoidal modulation mechanism of the contrast transfer functionthat correlates with both defocus values and skyrmion dimensions. Our findings establish quantitative limitations of conventional Fresnel contrast analysis for ultrasmall skyrmions while revealing fundamental insights into defocus-mediated phase-to-intensity conversion processes in nanoscale magnetic imaging.

cond-mat.mtrl-sci↗

arXiv2025

Tunable Asymmetric Delay Attack in Quantum Clock Synchronization

Quantum clock synchronization underpins modern secure communications and critical infrastructure, yet its fundamental dependence on channel reciprocity introduces an exploitable vulnerability to asymmetric delay attacks. Current attack strategies rely on static delays, limiting their ability to target application-specific stability requirements. Here, we propose a tunable asymmetric delay attack (T-ADA) that dynamically controls delay parameters to induce manipulate synchronization accuracy. Through experimental implementation, we demonstrate how tailored attack trajectories can selectively compromise system stability across different scenarios. This work uncovers key vulnerabilities in synchronization protocols under customizable attacks and provide a foundation for developing secure and resilient quantum clock synchronization systems.

quant-ph↗

arXiv2025

Unified Multimodal Models as Auto-Encoders

Image-to-text (I2T) understanding and text-to-image (T2I) generation are two fundamental, important yet traditionally isolated multimodal tasks. Despite their intrinsic connection, existing approaches typically optimize them independently, missing the opportunity for mutual enhancement. In this paper, we argue that the both tasks can be connected under a shared Auto-Encoder perspective, where text serves as the intermediate latent representation bridging the two directions - encoding images into textual semantics (I2T) and decoding text back into images (T2I). Our key insight is that if the encoder truly "understands" the image, it should capture all essential structure, and if the decoder truly "understands" the text, it should recover that structure faithfully. Building upon this principle, we propose Unified-GRPO, a post-training method based on reinforcement learning that jointly optimizes both modules through reconstructive rewards, maximizing the semantic consistency between the input and the generated images. Under this reconstruction objective, the encoder is encouraged to extract as much accurate and comprehensive semantic information from the input image to maximize reconstruction quality, while the decoder is simultaneously optimized to generate conditioned on the encoder's prior, enabling a self-evolving improvement. Empirically, we find that using text as the intermediate representation and training under a reconstructive RL paradigm effectively benefits both I2T and T2I. The I2T module gains stronger fine-grained visual perception, such as small-object recognition, grounding, etc, while its dense embeddings and language priors, in turn, provide richer semantic signals that improve T2I fidelity and complex instruction following. These results demonstrate that the reconstructive RL establishes a mutually reinforcing cross-modal synergy within the auto-encoding framework.

cs.CV↗

arXiv2025

AI Must not be Fully Autonomous

Autonomous Artificial Intelligence (AI) has many benefits. It also has many risks. In this work, we identify the 3 levels of autonomous AI. We are of the position that AI must not be fully autonomous because of the many risks, especially as artificial superintelligence (ASI) is speculated to be just decades away. Fully autonomous AI, which can develop its own objectives, is at level 3 and without responsible human oversight. However, responsible human oversight is crucial for mitigating the risks. To ague for our position, we discuss theories of autonomy, AI and agents. Then, we offer 12 distinct arguments and 6 counterarguments with rebuttals to the counterarguments. We also present 15 pieces of recent evidence of AI misaligned values and other risks in the appendix.

cs.AI↗

arXiv2025

Clock Synchronization for Drone-Based Entanglement Quantum Key Distribution

Drone-based entanglement distribution provides full spatiotemporal coverage for quantum networks, enabling quantum key distribution (QKD) in dynamic environments. The security of QKD fundamentally depends on high-fidelity quantum state measurements, for which high-precision clock synchronization is indispensable, as timing jitter is inversely correlated with quantum state fidelity. However, drone-based clock synchronization is constrained by SWaP (Size, Weight, and Power) limitations and dynamic mobility effects. Here, we propose a synchronization protocol for drone-based entanglement distribution, leveraging nanosecond-accurate Global Navigation Satellite System (GNSS) timing and entanglement-based timing correction to overcome SWaP constraints. Experimental results demonstrate 24 ps RMS synchronization in simulated free-space quantum channels with distance dynamics, without requiring precision reference clock. Our protocol enables drone-based entanglement distribution, paving the way for seamless wide-area and local-area quantum internet.

quant-ph↗

arXiv2025

Video-Bench: Human-Aligned Video Generation Benchmark

Video generation assessment is essential for ensuring that generative models produce visually realistic, high-quality videos while aligning with human expectations. Current video generation benchmarks fall into two main categories: traditional benchmarks, which use metrics and embeddings to evaluate generated video quality across multiple dimensions but often lack alignment with human judgments; and large language model (LLM)-based benchmarks, though capable of human-like reasoning, are constrained by a limited understanding of video quality metrics and cross-modal consistency. To address these challenges and establish a benchmark that better aligns with human preferences, this paper introduces Video-Bench, a comprehensive benchmark featuring a rich prompt suite and extensive evaluation dimensions. This benchmark represents the first attempt to systematically leverage MLLMs across all dimensions relevant to video generation assessment in generative models. By incorporating few-shot scoring and chain-of-query techniques, Video-Bench provides a structured, scalable approach to generated video evaluation. Experiments on advanced models including Sora demonstrate that Video-Bench achieves superior alignment with human preferences across all dimensions. Moreover, in instances where our framework's assessments diverge from human evaluations, it consistently offers more objective and accurate insights, suggesting an even greater potential advantage over traditional human judgment.

cs.CV↗

arXiv2024

Anomalous-Hall Neel textures in altermagnetic materials

Recently, the altermagnets, a new kind of collinear antiferromagnet with nearly zero net magnetization and momentum-dependent spin-splitting of bands, have sparked great interest. Despite simple magnetic structures, these altermagnets exhibit intriguing and intricate dependence of anomalous Hall effect (AHE) on the N\'eel vector, in contrast to the conventional perpendicular configuration of Hall current with magnetization in ferromagnets. However, the fundamental relationship between the AHE and the N\'eel vector remains largely elusive. Here, we reveal all the unconventional anomalous Hall textures in the N\'eel vector space, dubbed anomalous-Hall N\'eel textures (AHNTs) for altermagnets. Specifically, we identify 10 types across four categories of AHNTs for all altermagnets. Notably, we find that AHNTs resemble the known spin textures in momentum space, and further reveal their symmetry origin. Meanwhile, we examine our key discoveries in prototypical altermagnets. Our work offers a thorough understanding of AHE in altermagnets and a complete and pictorial classification of altermagnets based on the geometry of response functions.

cond-mat.mtrl-sci↗

arXiv2024

10 GHz Robust polarization modulation towards high-speed satellite-based quantum communication

In practical satellite-based quantum key distribution (QKD) systems, the preparation and transmission of polarization-encoding photons suffer from complex environmental effects and high channel-loss. Consequently, the hinge to enhancing the secure key rate (SKR) lies in achieving robust, low-error and high-speed polarization modulation. Although the schemes that realize self-compensation exhibit remarkable robustness. Their modulation speed is constrained to approximately 2 GHz to avoid the interaction between the electrical signal and the reverse optical pulses. Here we utilize the non-reciprocity of the lithium niobate modulators and eliminate the modulation on the reverse optical pulses. As this characteristic is widely available in the radio-frequency band, the modulation speed is no longer limited by the self-compensating optics and can be further increased. The measured average intrinsic QBER of the different polarization states at 10 GHz system repetition frequency is as low as 0.53% over 10 min without any compensation. And the experiment simulation shows that the proposed scheme extends the transmission distance to more than 350 km. Our work can be be efficient performed to the high-speed and high-loss satellite-based quantum communication scenario.

quant-ph↗

arXiv2024

Skin Effect of Nonlinear Optical Responses in Antiferromagnets

Nonlinear optics plays important roles in the research of fundamental physics and the applications of high-performance optoelectronic devices. The bulk nonlinear optical responses arise from the uniform light absorption in noncentrosymmetric crystals, and hence are usually considered to be the collective phenomena of all atoms. Here we show, in contrast to this common expectation, the nonlinear optical responses in antiferromagnets can be selectively accumulated near the surfaces, representing a skin effect. This is because the inversion symmetry, despite being broken globally by magnetism, is barely violated locally deeply inside these antiferromagnets. Using A-type layered antiferromagnets as the representatives, we predict that the spatial-dependent nonlinear optical responses, such as bulk photovoltaic effect (BPVE) and second harmonic generation (SHG), are notable in the top- and bottom-most layers and decay rapidly when moving away from the surfaces. Such a phenomenon is strongly associated with the antiferromagnetism and exists in a broad range of antiferromagnets composed of centrosymmetric sublattices, offering promising device applications using these antiferromagnets. Our work uncovers a previously overlooked property of nonlinear optical responses and opens new opportunities for high-performance antiferromagnetic optospintronics.

cond-mat.mtrl-sci↗

arXiv2024

Efficient Arbitrated Quantum Digital Signature with Multi-Receiver Verification

Quantum digital signature is used to authenticate the identity of the signer with information theoretical security, while providing non-forgery and non-repudiation services. In traditional multi-receiver quantum digital signature schemes without an arbitrater, the transferability of one-to-one signature is always required to achieve unforgeability, with complicated implementation and heavy key consumption. In this article, we propose an arbitrated quantum digital signature scheme, in which the signature can be verified by multiple receivers simultaneously, and meanwhile, the transferability of the signature is still kept. Our scheme can be simplified performed to various quantum secure networks, due to the proposed efficient signature calculation procedure with low secure key consumption and low computation complexity, by employing one-time universal hashing algorithm and one-time pad encryption scheme. The evaluation results show that our scheme uses at least two orders of magnitude less key than existing signature schemes with transferability when signing files of the same length with the same number of receivers and security parameter settings.

quant-ph↗

arXiv2024

Differentiated Service Entanglement Routing for Quantum Networks

The entanglement distribution networks with various topologies are mainly implemented by active wavelength multiplexing routing strategies. However, designing an entanglement routing scheme, which achieves the maximized network connections and the optimal overall network efficiency simultaneously, remains a huge challenge for quantum networks. In this article, we propose a differentiated service entanglement routing (DSER) scheme, which firstly finds out the lowest loss paths and supported wavelength channels with the tensor-based path searching algorithm, and then allocates the paired channels with the differentiated routing strategies. The evaluation results show that the proposed DSER scheme can be performed for constructing various large scale quantum networks.

quant-ph↗

arXiv2024

Deep Learning-Based Detection for Marker Codes over Insertion and Deletion Channels

Marker code is an effective coding scheme to protect data from insertions and deletions. It has potential applications in future storage systems, such as DNA storage and racetrack memory. When decoding marker codes, perfect channel state information (CSI), i.e., insertion and deletion probabilities, are required to detect insertion and deletion errors. Sometimes, the perfect CSI is not easy to obtain or the accurate channel model is unknown. Therefore, it is deserved to develop detecting algorithms for marker code without the knowledge of perfect CSI. In this paper, we propose two CSI-agnostic detecting algorithms for marker code based on deep learning. The first one is a model-driven deep learning method, which deep unfolds the original iterative detecting algorithm of marker code. In this method, CSI become weights in neural networks and these weights can be learned from training data. The second one is a data-driven method which is an end-to-end system based on the deep bidirectional gated recurrent unit network. Simulation results show that error performances of the proposed methods are significantly better than that of the original detection algorithm with CSI uncertainty. Furthermore, the proposed data-driven method exhibits better error performances than other methods for unknown channel models.

cs.IT↗

arXiv2023

Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN). However, most HGNNs follow a semi-supervised learning manner, which notably limits their wide use in reality since labels are usually scarce in real applications. Recently, contrastive learning, a self-supervised method, becomes one of the most exciting learning paradigms and shows great potential when there are no labels. In this paper, we study the problem of self-supervised HGNNs and propose a novel co-contrastive learning mechanism for HGNNs, named HeCo. Different from traditional contrastive learning which only focuses on contrasting positive and negative samples, HeCo employs cross-view contrastive mechanism. Specifically, two views of a HIN (network schema and meta-path views) are proposed to learn node embeddings, so as to capture both of local and high-order structures simultaneously. Then the cross-view contrastive learning, as well as a view mask mechanism, is proposed, which is able to extract the positive and negative embeddings from two views. This enables the two views to collaboratively supervise each other and finally learn high-level node embeddings. Moreover, to further boost the performance of HeCo, two additional methods are designed to generate harder negative samples with high quality. Besides the invariant factors, view-specific factors complementally provide the diverse structure information between different nodes, which also should be contained into the final embeddings. Therefore, we need to further explore each view independently and propose a modified model, called HeCo++. Specifically, HeCo++ conducts hierarchical contrastive learning, including cross-view and intra-view contrasts, which aims to enhance the mining of respective structures.

cs.LG↗

arXiv2022

Classification of second harmonic generation effect in magnetically ordered materials

The relationship between magnetic order and the second harmonic generation (SHG) effect is a fundamental area of study in condensed matter physics with significant practical implications. In order to gain a clearer understanding of this intricate relation, this study presents a comprehensive classification scheme for the SHG effect in magnetically ordered materials. This framework offers a straightforward approach to connect magnetic order and SHG effect. The characteristics of the SHG tensors in all magnetic point groups are studied using the isomorphic group method, followed by a comprehensive SHG effect classification scheme that includes seven types based on the symmetries of the magnetic phases and their corresponding parent phases. In addition, a tensor dictionary containing the SHG and linear magneto-optic (LMO) effect is established. Furthermore, an extensive SHG database of magnetically ordered materials is also built up. This classification strategy exposes an anomalous SHG effect with even characteristic under time-reversal symmetry, which is solely contributed by magnetic structure. Moreover, the proposed classification scheme facilitates the determination of magnetic structures through SHG effect.

cond-mat.mtrl-sci↗