arXiv subjects

Qian Niu

Publications and source records attributed to Qian Niu.

At least 73 records · Page 4Linked to original sources

arXiv2025

Emergence of Nodal-Knot Transitions by Disorder

Under certain symmetries, degenerate points in three-dimensional metals form one-dimensional nodal lines. These nodal lines sometimes exhibit intricate knotted structures and have been studied in various contexts. As one of the most common physical perturbations, disorder effects often trigger novel quantum phase transitions. For nodal-knot phases, whether disorder can drive knot transitions remains an open and intriguing question. Employing renormalization-group calculations, we demonstrate that nodal-knot transitions emerge in the presence of weak disorder. Specifically, both chemical-potential-type and magnetic-type disorders can induce knot transitions, resulting in the emergence of distinct knot topologies. The transition can be quantitatively characterized by changes in topological invariants such as the knot Wilson loop integrals. Our findings open up a new avenue for manipulating the topology of nodal-knot phases through disorder effects.

cond-mat.mes-hall↗

arXiv2025

Pairing-induced Momentum-space Magnetism and Its Implication In Optical Anomalous Hall Effect In Chiral Superconductors

The intrinsic mechanisms of the magneto-optical Kerr signal in chiral superconductors often involve multi-orbital degree of freedom. Here by considering a generic single-orbital and spinful Hamiltonian, we generalize the Onsager's relation to obtain the necessary conditions for the optical anomalous Hall effect. Using the down-folding method, we identify two types of effective momentum-space magnetism responsible for the optical anomalous Hall conductivity from non-unitary and unitary pairing potentials respectively. The former is due to the angular momentum of Cooper pair, while the latter requires the participation of the spin-orbit coupling in the normal state and has been largely overlooked previously. Using concrete examples, we show that the unitary pairing can lead to both ferromagnetism and complicated antiferromagnetic spin texture in the momentum space, resulting in an in-plane optical anomalous Hall effect with the magnetism parallel to the Hall-deflection plane. Our work reveals the essential role of spin degree of freedom in the optical anomalous Hall effect.

cond-mat.supr-con↗

arXiv2025

KokushiMD-10: Benchmark for Evaluating Large Language Models on Ten Japanese National Healthcare Licensing Examinations

Recent advances in large language models (LLMs) have demonstrated notable performance in medical licensing exams. However, comprehensive evaluation of LLMs across various healthcare roles, particularly in high-stakes clinical scenarios, remains a challenge. Existing benchmarks are typically text-based, English-centric, and focus primarily on medicines, which limits their ability to assess broader healthcare knowledge and multimodal reasoning. To address these gaps, we introduce KokushiMD-10, the first multimodal benchmark constructed from ten Japanese national healthcare licensing exams. This benchmark spans multiple fields, including Medicine, Dentistry, Nursing, Pharmacy, and allied health professions. It contains over 11588 real exam questions, incorporating clinical images and expert-annotated rationales to evaluate both textual and visual reasoning. We benchmark over 30 state-of-the-art LLMs, including GPT-4o, Claude 3.5, and Gemini, across both text and image-based settings. Despite promising results, no model consistently meets passing thresholds across domains, highlighting the ongoing challenges in medical AI. KokushiMD-10 provides a comprehensive and linguistically grounded resource for evaluating and advancing reasoning-centric medical AI across multilingual and multimodal clinical tasks.

cs.CL↗

arXiv2025

Geodynamics and artificial gravity in space-time crystals under slow perturbation and deformation

We present a theory of geodynamics in a space-time crystal based on an event wave packet constructed from the Floquet-Bloch waves, which not only involve a scalar dispersion function but also a Berry curvature tensor in the phase space manifold of space-time and the reciprocal quasi energy-momentum. In the presence of structural deformation, this theory is naturally extended into a covariant form with the introduction of a lattice connection constructed out of the gradients of the local lattice vectors. The geodesic equation for a particle not only involves the lattice connection but also higher-order corrections from space-time inhomogeneity of Berry curvatures and quasi energy-momentum dispersion gradients. These emergent connections and metric terms in the geodesic equations indicate the potential to experimentally realize artificial gravitational effects, thereby establishing a direct conceptual link between general relativity and quantum theory.

cond-mat.mtrl-sci↗

arXiv2025

Magic of nonlocal geometric force: lighting up optical transition and transporting angular momentum by chiral phonons

We investigate the impact of the nonlocal geometric force -- arising from the molecular Berry curvature -- on the lattice dynamics of magnetic materials with broken time-reversal symmetry. A first-principles computational framework is established to evaluate this force across the entire Brillouin zone. We apply it to monolayer CoCl$_2$, a ferromagnetic half-semiconductor with a narrow bandgap forbidding direct dipolar optical transition. At the phonon Brillouin zone center, the pronounced nonlocal geometric force leads to a splitting of the two upper optical phonon branches by $3 \times 10^{-2}$ THz, transforming the phonons into chiral modes. Optical chiral phonons can light up the intravalley dark exciton via absorpting circularly polarized photons. Furthermore, acoustic chiral phonons induced by the nonlocal geometric force can transport angular momentum and contribute to a non-dissipative phonon Hall viscosity.

cond-mat.mtrl-sci↗

arXiv2025

Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models

Knowledge distillation (KD) is a technique for transferring knowledge from complex teacher models to simpler student models, significantly enhancing model efficiency and accuracy. It has demonstrated substantial advancements in various applications including image classification, object detection, language modeling, text classification, and sentiment analysis. Recent innovations in KD methods, such as attention-based approaches, block-wise logit distillation, and decoupling distillation, have notably improved student model performance. These techniques focus on stimulus complexity, attention mechanisms, and global information capture to optimize knowledge transfer. In addition, KD has proven effective in compressing large language models while preserving accuracy, reducing computational overhead, and improving inference speed. This survey synthesizes the latest literature, highlighting key findings, contributions, and future directions in knowledge distillation to provide insights for researchers and practitioners on its evolving role in artificial intelligence and machine learning.

cs.CL↗

arXiv2025

Fully First-Principles Approach in Studying Topological Magnons

We develop a fully first-principles approach for spin dynamics based on density functional perturbation theory. We demonstrate that the magnon wavefunction can be expressed by a set of electronic wavefunctions obtained from the decomposition of magnon density profile, enabling the direct calculation of magnonic quantities including Berry curvature and Chern number. As a concrete example, we show that monolayer CrI$_3$ can host topological magnons driven by spin-orbit coupling. Our model-free approach paves the way for the comprehensive studies of magnons in real materials.

cond-mat.mtrl-sci↗

arXiv2025

Advanced Deep Learning Methods for Protein Structure Prediction and Design

After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules. The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture, thereby illustrating the current state of the art in computational protein modelling. Subsequent chapters focus on practical applications, presenting case studies that range from individual protein predictions to complex biomolecular interactions. Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored. The later sections review the industry landscape of protein design, highlighting the transformative role of artificial intelligence in biotechnology and discussing emerging market trends and future challenges. Supplementary appendices provide essential resources such as databases and open source tools, making this volume a valuable reference for researchers and students.

q-bio.BM↗

arXiv2025

Space-time Supercrystals from Non-Abelian Electric Translation Symmetries

Electronic supercrystals can form in spatial or temporal dimensions where traditional mechanisms usually require many-body interactions, such as Wigner crystals or discrete time crystals. We propose a novel approach for electronic supercrystals in 1+1D without requiring many-body interactions, but arising from the competition between characteristic space-time areas in periodically driven Su-Schrieffer-Heeger lattices under electric fields. Utilizing the non-Abelian dynamical symmetries described by the electric translation group, the area competition is characterized by fractal spectra and replicated bands, furnished with eigenstates crystallized in enlarged space-time unit cells forming space-time supercrystals under perturbations. We also report robust dynamical localization of electrons arising from the static topology of the SSH lattice.

cond-mat.other↗

arXiv2025

Generative Adversarial Networks Bridging Art and Machine Intelligence

Generative Adversarial Networks (GAN) have greatly influenced the development of computer vision and artificial intelligence in the past decade and also connected art and machine intelligence together. This book begins with a detailed introduction to the fundamental principles and historical development of GANs, contrasting them with traditional generative models and elucidating the core adversarial mechanisms through illustrative Python examples. The text systematically addresses the mathematical and theoretical underpinnings including probability theory, statistics, and game theory providing a solid framework for understanding the objectives, loss functions, and optimisation challenges inherent to GAN training. Subsequent chapters review classic variants such as Conditional GANs, DCGANs, InfoGAN, and LAPGAN before progressing to advanced training methodologies like Wasserstein GANs, GANs with gradient penalty, least squares GANs, and spectral normalisation techniques. The book further examines architectural enhancements and task-specific adaptations in generators and discriminators, showcasing practical implementations in high resolution image generation, artistic style transfer, video synthesis, text to image generation and other multimedia applications. The concluding sections offer insights into emerging research trends, including self-attention mechanisms, transformer-based generative models, and a comparative analysis with diffusion models, thus charting promising directions for future developments in both academic and applied settings.

cs.LG↗

arXiv2025

Chern Number Tunable Quantum Anomalous Hall Effect in Compensated Antiferromagnets

We propose to realize the quantum anomalous Hall effect (QAHE) in two-dimensional compensated antiferromagnets without net spin magnetization.} We consider antiferromagnetic MnBi$_2$Te$_4$ as a concrete example. \textcolor{blue}{By breaking the parity-time ($\mathcal{PT}$) symmetry of even-layer MnBi$_2$Te$_4$, we find that the system can host the QAHE with a nonzero Chern number.} We show that by controlling the antiferromagnetic spin configuration, for example, down/up/up/down that breaks $\mathcal{PT}$ symmetry, tetralayer MnBi$_2$Te$_4$ can host a Chern number $\mathcal{C}=-1$. Such spin configuration can be stabilized by pinning the spin orientations of the surfaces. \textcolor{blue}{Furthermore, via tuning the on-site orbital energy and vertical electric fields, we find rich QAHE phases with tunable Chern number of $|\mathcal{C}|=1, 2, 3$. In addition, we reveal that the edge states are layer-selective and primarily locate at the boundaries of the bottom and top layers. Our work not only proposes a scheme to realize Chern number tunable QAHE in antiferromagnets without net spin magnetization, but also provides a platform for layer-selective dissipationless transport devices.

cond-mat.mes-hall↗

arXiv2025

Orthogonal Geometry of Magneto-Optical Kerr Effect Enabled by Magnetization Multipole of Berry Curvature

The Magneto-Optical Kerr Effect (MOKE) is a fundamental tool in magnetometry, pivotal for advancing research in optics, magnetism, and spintronics as a direct probe of magnetization. Traditional MOKE measurements primarily detect the magnetization components parallel to the Poynting vector, which can only access the magnitude but not the direction of the orthogonal component. In this study, we introduce an orthogonal MOKE geometry in which the Kerr signal detects both the magnitude and direction of the magnetization component perpendicular to the Poynting vector. We demonstrate the broad applicability of this orthogonal geometry through the MOKE measurements in cubic ferromagnets and van der Waals ferromagnet. We theoretically show that the orthogonal MOKE geometry is enabled by the multipolar structure of Berry curvature in the magnetization space, which generally induces a Voigt vector orthogonal to the magnetization, thereby accounting for the unique magnetization angle dependence distinct from conventional MOKE. The establishment of the orthogonal MOKE geometry not only introduces a new paradigm for magneto-optical measurements but also provides a framework for exploring the magnetization multipoles of Berry curvature across the electromagnetic spectrum.

physics.optics↗

arXiv2025

From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financial technology, where decision-making processes must account for uncertainty. This review explores the evolution of uncertainty quantification techniques in AI, distinguishing between aleatoric and epistemic uncertainties, and discusses the mathematical foundations and methods used to quantify these uncertainties. We provide an overview of advanced techniques, including probabilistic methods, ensemble learning, sampling-based approaches, and generative models, while also highlighting hybrid approaches that integrate domain-specific knowledge. Furthermore, we examine the diverse applications of UQ across various fields, emphasizing its impact on decision-making, predictive accuracy, and system robustness. The review also addresses key challenges such as scalability, efficiency, and integration with explainable AI, and outlines future directions for research in this rapidly developing area. Through this comprehensive survey, we aim to provide a deeper understanding of UQ's role in enhancing the reliability, safety, and trustworthiness of AI systems.

cs.AI↗

arXiv2024

Deep Learning Model Security: Threats and Defenses

Deep learning has transformed AI applications but faces critical security challenges, including adversarial attacks, data poisoning, model theft, and privacy leakage. This survey examines these vulnerabilities, detailing their mechanisms and impact on model integrity and confidentiality. Practical implementations, including adversarial examples, label flipping, and backdoor attacks, are explored alongside defenses such as adversarial training, differential privacy, and federated learning, highlighting their strengths and limitations. Advanced methods like contrastive and self-supervised learning are presented for enhancing robustness. The survey concludes with future directions, emphasizing automated defenses, zero-trust architectures, and the security challenges of large AI models. A balanced approach to performance and security is essential for developing reliable deep learning systems.

cs.CR↗

arXiv2024

A Comprehensive Guide to Explainable AI: From Classical Models to LLMs

Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making processes. This book offers a comprehensive guide to XAI, bridging foundational concepts with advanced methodologies. It explores interpretability in traditional models such as Decision Trees, Linear Regression, and Support Vector Machines, alongside the challenges of explaining deep learning architectures like CNNs, RNNs, and Large Language Models (LLMs), including BERT, GPT, and T5. The book presents practical techniques such as SHAP, LIME, Grad-CAM, counterfactual explanations, and causal inference, supported by Python code examples for real-world applications. Case studies illustrate XAI's role in healthcare, finance, and policymaking, demonstrating its impact on fairness and decision support. The book also covers evaluation metrics for explanation quality, an overview of cutting-edge XAI tools and frameworks, and emerging research directions, such as interpretability in federated learning and ethical AI considerations. Designed for a broad audience, this resource equips readers with the theoretical insights and practical skills needed to master XAI. Hands-on examples and additional resources are available at the companion GitHub repository: https://github.com/Echoslayer/XAI_From_Classical_Models_to_LLMs.

cs.LG↗

arXiv2024

Deep Learning, Machine Learning, Advancing Big Data Analytics and Management

Advancements in artificial intelligence, machine learning, and deep learning have catalyzed the transformation of big data analytics and management into pivotal domains for research and application. This work explores the theoretical foundations, methodological advancements, and practical implementations of these technologies, emphasizing their role in uncovering actionable insights from massive, high-dimensional datasets. The study presents a systematic overview of data preprocessing techniques, including data cleaning, normalization, integration, and dimensionality reduction, to prepare raw data for analysis. Core analytics methodologies such as classification, clustering, regression, and anomaly detection are examined, with a focus on algorithmic innovation and scalability. Furthermore, the text delves into state-of-the-art frameworks for data mining and predictive modeling, highlighting the role of neural networks, support vector machines, and ensemble methods in tackling complex analytical challenges. Special emphasis is placed on the convergence of big data with distributed computing paradigms, including cloud and edge computing, to address challenges in storage, computation, and real-time analytics. The integration of ethical considerations, including data privacy and compliance with global standards, ensures a holistic perspective on data management. Practical applications across healthcare, finance, marketing, and policy-making illustrate the real-world impact of these technologies. Through comprehensive case studies and Python-based implementations, this work equips researchers, practitioners, and data enthusiasts with the tools to navigate the complexities of modern data analytics. It bridges the gap between theory and practice, fostering the development of innovative solutions for managing and leveraging data in the era of artificial intelligence.

cs.LG↗

arXiv2024

Observation of Giant Nernst plateau in ideal 1D Weyl Phase

The search for a giant Nernst effect beyond conventional mechanisms offers advantages for developing advanced thermoelectric devices and understanding charge-entropy conversion. Here, we study the Seebeck and Nernst effects of HfTe5 over a wide range of magnetic fields. By tracking the unusual magneto-thermoelectric responses, we reveal two magnetic-field-driven phase transitions proposed for weak topological insulators: the gap-closing transition of the zeroth Landau bands and the topological Lifshitz transition. After the magnetic fields exceed approximately ten times the quantum limit, we observe that the Nernst signal no longer varies with the fields, forming a plateau with a remarkably large value, reaching up to 50 μV/K at 2 K. We theoretically explain the giant Nernst plateau as a unique signature of the ideal 1D Weyl phase formed in such high fields. Our findings expand the understanding of ideal Weyl physics and open new avenues for realizing novel thermoelectric effects without fundamental constraints.

cond-mat.mtrl-sci↗

arXiv2024

Multipolar Anisotropy in Anomalous Hall Effect from Spin-Group Symmetry Breaking

Traditional view of the anomalous Hall effect~(AHE) in ferromagnets is that it arises from the magnetization perpendicular to the measurement plane and that there is a linear dependence on the latter. Underlying such a view is the thinking that the AHE is a time-reversal symmetry breaking phenomenon and can therefore be treated in terms of a power series in the magnetic order. However, this view is squarely challenged by a number of experiments recently, urging for a thorough theoretical investigation on the fundamental level. We find that for strong magnets, it is more appropriate and fruitful to regard the AHE as a spin-group symmetry breaking phenomenon where the critical parameter is the spin-orbit interaction strength, which involves a much smaller energy scale. In ferromagnets, the spin-orbit coupling breaks the $\infty 2^\prime$ spin rotation symmetry, and the key to characterizing such symmetry breaking is the identification of spin-orbit vectors which transform regularly under spin group operations. Born out of our framework is a rich multi-polar relationship between the anomalous Hall conductivity and the magnetization direction, with each pole being expanded progressively in powers of the spin-orbit coupling strength. For the leading order contribution, i.e., the dipole, its isotropic part corresponds to the traditional view, and its anisotropic part can lead to the in-plane AHE where the magnetization lies within the measurement plane. Beyond the dipolar one, the octupolar structure offers the leading order source of nonlinearity and hence introduces unique anisotropy where the dipolar structure cannot. The dipolar and octupolar structure offers a unified explanation for the in-plane AHE recently observed in various ferromagnets, and our comprehensive analysis further extends the candidate material systems.

cond-mat.mtrl-sci↗