arXiv subjects

Qian Niu

Publications and source records attributed to Qian Niu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Extrinsic Mechanisms of Phonon Magnetic Moment

We develop a general formalism of phonon magnetic moment by including the relaxation process. We then identify the skew-scattering and side-jump contributions to the phonon magnetic moment originating from the non-adiabaticity, both of which are related to the nonlocal phonon Berry curvature and are in close analogy to those in the electronic Hall effect. All contributions of the phonon magnetic moment are exemplified in a honeycomb lattice, showing that the extrinsic contribution can be as important as the intrinsic one and that the resulting phonon angular momentum varies significantly across the Brillouin zone. Our work offers a systematic framework of the phonon chirality and paves the way of tuning the phonon magnetic moment through the non-adiabaticity.

cond-mat.mes-hall↗

arXiv2026

CORA: Conformal Risk-Controlled Agents for Safeguarded Mobile GUI Automation

Graphical user interface (GUI) agents powered by vision language models (VLMs) are rapidly moving from passive assistance to autonomous operation. However, this unrestricted action space exposes users to severe and irreversible financial, privacy or social harm. Existing safeguards rely on prompt engineering, brittle heuristics and VLM-as-critic lack formal verification and user-tunable guarantees. We propose CORA (COnformal Risk-controlled GUI Agent), a post-policy, pre-action safeguarding framework that provides statistical guarantees on harmful executed actions. CORA reformulates safety as selective action execution: we train a Guardian model to estimate action-conditional risk for each proposed step. Rather than thresholding raw scores, we leverage Conformal Risk Control to calibrate an execute/abstain boundary that satisfies a user-specified risk budget and route rejected actions to a trainable Diagnostician model, which performs multimodal reasoning over rejected actions to recommend interventions (e.g., confirm, reflect, or abort) to minimize user burden. A Goal-Lock mechanism anchors assessment to a clarified, frozen user intent to resist visual injection attacks. To rigorously evaluate this paradigm, we introduce Phone-Harm, a new benchmark of mobile safety violations with step-level harm labels under real-world settings. Experiments on Phone-Harm and public benchmarks against diverse baselines validate that CORA improves the safety--helpfulness--interruption Pareto frontier, offering a practical, statistically grounded safety paradigm for autonomous GUI execution. Code and benchmark are available at cora-agent.github.io.

cs.LG↗

arXiv2026

Beyond In-Distribution Success: Scaling Curves of CoT Granularity for Language Model Generalization

Generalization to novel compound tasks under distribution shift is important for deploying transformer-based language models (LMs). This work investigates Chain-of-Thought (CoT) reasoning as a means to enhance OOD generalization. Through controlled experiments across several compound tasks, we reveal three key insights: (1) While QA-trained models achieve near-perfect in-distribution accuracy, their OOD performance degrades catastrophically, even with 10000k+ training examples; (2) the granularity of CoT data strongly correlates with generalization performance; finer-grained CoT data leads to better generalization; (3) CoT exhibits remarkable sample efficiency, matching QA performance with much less (even 80%) data. Theoretically, we demonstrate that compound tasks inherently permit shortcuts in Q-A data that misalign with true reasoning principles, while CoT forces internalization of valid dependency structures, and thus can achieve better generalization. Further, we show that transformer positional embeddings can amplify generalization by emphasizing subtask condition recurrence in long CoT sequences. Our combined theoretical and empirical analysis provides compelling evidence for CoT reasoning as a crucial training paradigm for enabling LM generalization under real-world distributional shifts for compound tasks.

cs.CL↗

arXiv2026

Magnon-Driven Anomalous Hall Effect in Altermagnets

We propose a magnon-driven anomalous Hall effect in altermagnets, arising from the coupling between coherently excited chiral magnons and chiral electronic motion. Using density-matrix perturbation theory and symmetry analysis, we show that the resulting Hall conductivity is solely determined by the chiralithy of the Néel-order precession, in sharp contrast to the anomalous Hall effect from the equilibrium Néel order. It then has distinct symmetry requirements from the latter and can exist even when the latter is forbidden by symmetry. The magnon-driven anomalous Hall effect is exemplified in a minimal lattice model with the same symmetry of the altermagnet CrSb, which hosts no static anomalous Hall effect. Our results reveal a direct interplay between chiral magnons and chiral electronic motion, paving the way of probing magnon chirality and to control electronic chirality through magnons.

cond-mat.mes-hall↗

arXiv2026

Eccentricity valley Hall effect

Valleytronics harnesses the valley degree of freedom -- energy-degenerate extrema in the electronic band structure -- for information storage and processing. Valley Hall effect (VHE) is a cornerstone of valleytronics, enabling electric generation of pure valley currents. While extensively studied in systems with valleys located at time-reversal-breaking points, here, we shift the paradigm to valleytronic platforms with time-reversal-invariant valleys (TRIVs), revealing a novel phenomenon: eccentricity VHE. Unlike conventional VHE, the valley Hall angle for eccentricity VHE is an intrinsic geometric property, governed solely by the eccentricity of the valley Fermi surface, rendering it highly robust against variations in temperature or carrier density. Eccentricity VHE emerges universally across all 25 layer groups supporting TRIVs. We demonstrate these distinctive features in monolayer GeS$_{2}$ via first-principles calculations, predicting a significant valley Hall angle of 0.74. This effect can be detected through nonlocal transport measurements exhibiting characteristic scaling behavior, or, in certain cases, through valley-layer coupling. Our findings reveal a critical overlooked facet of valley Hall physics, transcend the established VHE paradigm, and significantly broadens the scope of valleytronics.

cond-mat.mes-hall↗

arXiv2026

Anisotropic Gyromagnetic Ratio and Orthogonal Einstein-de Haas Effect

We theoretically demonstrate an orthogonal Einstein-de Haas effect, where the rotation of ferromagnetic materials is caused by the change of magnetization in the direction orthogonal to the rotation axis. This amounts to an anisotropic gyromagnetic ratio. To reveal its microscopic origin, we treat the spin-orbit coupling as a perturbation, integrate out the electronic degree of freedom, and show that in collinear ferromagnets the phonon angular momentum admits a dipolar structure in the spin-order space due to the constraint of the spin group symmetry. The spin-flipping and spin-conserving parts of the spin-orbit coupling contribute differently to such a dipolar structure. All these features are exemplified in a lattice electron-phonon model with ferromagnetic order and $C_{1h}$ point group symmetry. Our work lays the ground for revealing the connection between phonon angular momentum and general spin-order configurations.

cond-mat.mes-hall↗

arXiv2025

Semiclassical theory for proximity-induced superconducting systems with spin-orbit coupling

We develop a semiclassical theory of superconducting quasiparticles for proximity-induced superconducting systems, where spin-orbit coupling plays a critical role in shaping the quasiparticle dynamics. We reveal the structure of superconducting Berry curvatures in such systems, and derived the superconducting Berry curvature induced thermal Edelstein effect and spin Nernst effect. We calculate these two thermo-spin responses with model systems where Rashba spin-orbit coupling, proximity induced superconductivity, and ferromagnetic order are coexisting.

cond-mat.supr-con↗

arXiv2025

Atomic-scale spin sensing of a 2D $d$-wave altermagnet via helical tunneling

Altermagnetism simultaneously possesses nonrelativistic spin responses and zero net magnetization, thus combining advantages of ferromagnetism and antiferromagnetism. This superiority originates from its unique dual feature, i.e., opposite-magnetic sublattices in real space and alternating spin polarization in momentum space enforced by the same crystal symmetry. Therefore, the determination of an altermagnetic order and its unique spin response inherently necessitates atomic-scale spin-resolved measurements in real and momentum spaces, an experimental milestone yet to be achieved. Here, via utilizing the helical edge (hinge) modes of a higher order topological insulator as the spin sensor, we realize spin-resolved scanning tunneling microscopy which enables us to pin down the dual-space feature of a layered $d$-wave altermagnet, KV$_2$Se$_2$O. In real space, atomic-registered mapping demonstrates the checkerboard antiferromagnetic order together with density-wave lattice modulation, and in momentum space, spin-resolved spectroscopic imaging provides a direct visualization of d-wave spin splitting of the band structure. Critically, using this new topology-guaranteed spin filter we directly reveal the unidirectional, spin-polarized quasiparticle excitations originating from the crystal symmetry-paired X and Y valleys around opposite magnetic sublattices simultaneously --the unique spin response for $d$-wave altermagnetism. Our experiments establish a solid basis for the exploration and utilization of altermagnetism in layered materials and further facilitate access to atomic-scale spin sensing and manipulating of 2D quantum materials.

cond-mat.mes-hall↗

arXiv2025

Frequency Domain Berry Curvature Effect on Time Refraction

We demonstrate that there exist frequency domain Berry curvature in the wave function of photons in dispersive optical systems. This property arises from the frequency dispersion of its dielectric function, which makes Maxwell equations a non-standard eigenvalue equation, with the eigenvalue (frequency) appearing inside the operator itself. We study this new Berry curvature effect on time refraction of magnetoplasmon-polariton as an example. It can induce deflection in the trajectory of a photon and make the ray swing.

cond-mat.mes-hall↗

arXiv2025

Experimental Evidence of Néel-order-driven Magneto-optical Kerr Effect in an Altermagnetic Insulator

The magneto-optical Kerr effect (MOKE) is investigated in hematite, a collinear antiferromagnetic insulator, across a broad wavelength spectrum. By combining the optical measurements with magnetometry results, we unambiguously demonstrate that the Néel-order contribution dominates the MOKE signal, while contributions from net magnetization and external magnetic fields are negligible. This conclusion is quantitatively supported by first-principles calculations, and qualitatively by a symmetry analysis that the Néel contribution appears at the first order in spin-orbit coupling while the magnetization contribution starts only at the third order. This study clarifies the altermagnetic origin of the pronounced MOKE in hematite, underscoring the potential of altermagnets as a promising new class of magneto-optical materials.

cond-mat.other↗

arXiv2025

Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Object-Oriented Programming

Object-Oriented Programming (OOP) has become a crucial paradigm for managing the growing complexity of modern software systems, particularly in fields like machine learning, deep learning, large language models (LLM), and data analytics. This work provides a comprehensive introduction to the integration of OOP techniques within these domains, with a focus on improving code modularity, maintainability, and scalability. We begin by outlining the evolution of computing and the rise of OOP, followed by an in-depth discussion of key OOP principles such as encapsulation, inheritance, polymorphism, and abstraction. The practical application of these principles is demonstrated using Python, a widely adopted language in AI and data science. Furthermore, we examine how design patterns and modular programming can be employed to enhance the structure and efficiency of machine learning systems. In subsequent sections, we apply these OOP concepts to real-world AI tasks, including the encapsulation of preprocessing workflows, machine learning model training, and evaluation. Detailed examples illustrate how OOP can be used to build reusable, scalable machine learning systems while maintaining code clarity and reducing redundancy.This work is intended to serve as a bridge for both beginners and experienced developers, equipping them with the necessary knowledge to apply OOP methodologies in AI-driven projects, ultimately fostering the development of more robust and maintainable systems.

cs.CL↗

arXiv2025

Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns

This book, Design Patterns in Machine Learning and Deep Learning: Advancing Big Data Analytics Management, presents a comprehensive study of essential design patterns tailored for large-scale machine learning and deep learning applications. The book explores the application of classical software engineering patterns, Creational, Structural, Behavioral, and Concurrency Patterns, to optimize the development, maintenance, and scalability of big data analytics systems. Through practical examples and detailed Python implementations, it bridges the gap between traditional object-oriented design patterns and the unique demands of modern data analytics environments. Key design patterns such as Singleton, Factory, Observer, and Strategy are analyzed for their impact on model management, deployment strategies, and team collaboration, providing invaluable insights into the engineering of efficient, reusable, and flexible systems. This volume is an essential resource for developers, researchers, and engineers aiming to enhance their technical expertise in both machine learning and software design.

cs.SE↗

arXiv2025

Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- Blockchain and Applications

A detailed exploration of blockchain technology and its applications across various fields is provided, beginning with an introduction to cryptography fundamentals, including symmetric and asymmetric encryption, and their roles in ensuring security and trust within blockchain systems. The structure and mechanics of Bitcoin and Ethereum are then examined, covering topics such as proof-of-work, proof-of-stake, and smart contracts. Practical applications of blockchain in industries like decentralized finance (DeFi), supply chain management, and identity authentication are highlighted. The discussion also extends to consensus mechanisms and scalability challenges in blockchain, offering insights into emerging technologies like Layer 2 solutions and cross-chain interoperability. The current state of academic research on blockchain and its potential future developments are also addressed.

cs.CR↗

arXiv2025

Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice

This book provides a comprehensive introduction to the foundational concepts of machine learning (ML) and deep learning (DL). It bridges the gap between theoretical mathematics and practical application, focusing on Python as the primary programming language for implementing key algorithms and data structures. The book covers a wide range of topics, including basic and advanced Python programming, fundamental mathematical operations, matrix operations, linear algebra, and optimization techniques crucial for training ML and DL models. Advanced subjects like neural networks, optimization algorithms, and frequency domain methods are also explored, along with real-world applications of large language models (LLMs) and artificial intelligence (AI) in big data management. Designed for both beginners and advanced learners, the book emphasizes the critical role of mathematical principles in developing scalable AI solutions. Practical examples and Python code are provided throughout, ensuring readers gain hands-on experience in applying theoretical knowledge to solve complex problems in ML, DL, and big data analytics.

cs.LG↗

arXiv2025

Magnon Thermal Hall Effect Induced By Symmetric Exchange Interaction

By analyzing the spin-group symmetry of magnons, we establish two generalized Onsager's relations in the magnon thermal Hall effect, which reveals the rich and complicated structures of the magnon Berry curvature in the parameter space of different types of the exchange coupling. As an important consequence, we find that the diagonal and off-diagonal part of the symmetric anisotropic exchange coupling together can support the planar magnon thermal Hall effect without the DM interaction. This removes the local inversion-symmetry-breaking condition for generating the magnon thermal Hall effect. Moreover, we predict an exotic phenomenon that the thermal conductivity exhibits angular dependence with respect to the in-plane magnetization. Our work lays the ground for decoding the coupling between magnon transport and different types of exchange interactions.

cond-mat.mes-hall↗

arXiv2025

Japanese AI Agent System on Human Papillomavirus Vaccination: System Design

Human papillomavirus (HPV) vaccine hesitancy poses significant public health challenges, particularly in Japan where proactive vaccination recommendations were suspended from 2013 to 2021. The resulting information gap is exacerbated by misinformation on social media, and traditional ways cannot simultaneously address individual queries while monitoring population-level discourse. This study aimed to develop a dual-purpose AI agent system that provides verified HPV vaccine information through a conversational interface while generating analytical reports for medical institutions based on user interactions and social media. We implemented a system comprising: a vector database integrating academic papers, government sources, news media, and social media; a Retrieval-Augmented Generation chatbot using ReAct agent architecture with multi-tool orchestration across five knowledge sources; and an automated report generation system with modules for news analysis, research synthesis, social media sentiment analysis, and user interaction pattern identification. Performance was assessed using a 0-5 scoring scale. For single-turn evaluation, the chatbot achieved mean scores of 4.83 for relevance, 4.89 for routing, 4.50 for reference quality, 4.90 for correctness, and 4.88 for professional identity (overall 4.80). Multi-turn evaluation yielded higher scores: context retention 4.94, topic coherence 5.00, and overall 4.98. The report generation system achieved completeness 4.00-5.00, correctness 4.00-5.00, and helpfulness 3.67-5.00, with reference validity 5.00 across all periods. This study demonstrates the feasibility of an integrated AI agent system for bidirectional HPV vaccine communication. The architecture enables verified information delivery with source attribution while providing systematic public discourse analysis, with a transferable framework for adaptation to other medical contexts.

cs.AI↗

arXiv2025

Deep Learning, Machine Learning -- Digital Signal and Image Processing: From Theory to Application

Digital Signal Processing (DSP) and Digital Image Processing (DIP) with Machine Learning (ML) and Deep Learning (DL) are popular research areas in Computer Vision and related fields. We highlight transformative applications in image enhancement, filtering techniques, and pattern recognition. By integrating frameworks like the Discrete Fourier Transform (DFT), Z-Transform, and Fourier Transform methods, we enable robust data manipulation and feature extraction essential for AI-driven tasks. Using Python, we implement algorithms that optimize real-time data processing, forming a foundation for scalable, high-performance solutions in computer vision. This work illustrates the potential of ML and DL to advance DSP and DIP methodologies, contributing to artificial intelligence, automated feature extraction, and applications across diverse domains.

cs.CV↗

arXiv2025

Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer

This book explores the role of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in driving the progress of big data analytics and management. The book focuses on simplifying the complex mathematical concepts behind deep learning, offering intuitive visualizations and practical case studies to help readers understand how neural networks and technologies like Convolutional Neural Networks (CNNs) work. It introduces several classic models and technologies such as Transformers, GPT, ResNet, BERT, and YOLO, highlighting their applications in fields like natural language processing, image recognition, and autonomous driving. The book also emphasizes the importance of pre-trained models and how they can enhance model performance and accuracy, with instructions on how to apply these models in various real-world scenarios. Additionally, it provides an overview of key big data management technologies like SQL and NoSQL databases, as well as distributed computing frameworks such as Apache Hadoop and Spark, explaining their importance in managing and processing vast amounts of data. Ultimately, the book underscores the value of mastering deep learning and big data management skills as critical tools for the future workforce, making it an essential resource for both beginners and experienced professionals.

cs.CL↗