arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 397 records · Page 22Linked to original sources

arXiv2025

The interplay of ferroelectricity and magneto-transport in non-magnetic moiré superlattices

The coupling of ferroelectricity and magnetic order provides rich tunability for engineering material properties and demonstrates great potential for uncovering novel quantum phenomena and multifunctional devices. Here, we report interfacial ferroelectricity in moiré superlattices constructed from graphene and hexagonal boron nitride. We observe ferroelectric polarization in an across-layer moiré superlattice with an intercalated layer, demonstrating a remnant polarization comparable to its non-intercalated counterpart. Remarkably, we reveal a magnetic-field enhancement of ferroelectric polarization that persists up to room temperature, showcasing an unconventional amplification of ferroelectricity in materials lacking magnetic elements. This phenomenon, consistent across devices with varying layer configurations, arises purely from electronic rather than ionic contributions. Furthermore, the ferroelectric polarization in turn modulates quantum transport characteristics, suppressing Shubnikov-de Haas oscillations and altering quantum Hall states in polarized phases. This interplay between ferroelectricity and magneto-transport in non-magnetic materials is crucial for exploring magnetoelectric effects and advancing two-dimensional memory and logic applications.

cond-mat.mtrl-sci↗

arXiv2025

A Fast and Accurate 3-D Reconstruction Algorithm for Near-Range Microwave Imaging with Handheld Synthetic Aperture Radar

The design of image reconstruction algorithms for near-range handheld synthetic aperture radar (SAR) systems has gained increasing popularity due to the promising performance of portable millimeter-wave (MMW) imaging devices in various application fields. Time domain imaging algorithms including the backprojection algorithm (BPA) and the Kirchhoff migration algorithm (KMA) are widely adopted due to their direct applicability to arbitrary scan trajectories. However, they suffer from time complexity issues that hinder their practical application. Wavenumber domain algorithms greatly improve the computational efficiency but most of them are restricted to specific array topologies. Based on the factorization techniques as adopted in far-field synthetic aperture radar imaging, the time domain fast factorized backprojection algorithm for handheld synthetic aperture radar (HHFFBPA) is proposed. The local spectral properties of the radar images for handheld systems are analyzed and analytical spectrum compression techniques are derived to realize efficient sampling of the subimages. Validated through numerical simulations and experiments, HHFFBPA achieves fast and accurate 3-D imaging for handheld synthetic aperture radar systems with arbitrary trajectories.

eess.SP↗

arXiv2025

Puzzles: Unbounded Video-Depth Augmentation for Scalable End-to-End 3D Reconstruction

Multi-view 3D reconstruction remains a core challenge in computer vision. Recent methods, such as DUST3R and its successors, directly regress pointmaps from image pairs without relying on known scene geometry or camera parameters. However, the performance of these models is constrained by the diversity and scale of available training data. In this work, we introduce Puzzles, a data augmentation strategy that synthesizes an unbounded volume of high-quality posed video-depth data from a single image or video clip. By simulating diverse camera trajectories and realistic scene geometry through targeted image transformations, Puzzles significantly enhances data variety. Extensive experiments show that integrating Puzzles into existing video-based 3D reconstruction pipelines consistently boosts performance without modifying the underlying network architecture. Notably, models trained on only ten percent of the original data augmented with Puzzles still achieve accuracy comparable to those trained on the full dataset. Code is available at https://jiahao-ma.github.io/puzzles/.

cs.CV↗

arXiv2025

Deep variational free energy prediction of dense hydrogen solid at 1200K

We perform deep variational free energy calculations to investigate the dense hydrogen system at 1200 K and high pressures. In this computational framework, neural networks are used to model the free energy through the proton Boltzmann distribution and the electron wavefunction. By directly minimizing the free energy, our results reveal the emergence of a crystalline order associated with the center of mass of hydrogen molecules at approximately 180 GPa. This transition from atomic liquid to a molecular solid is marked by discontinuities in both the pressure and thermal entropy. Additionally, we discuss the broader implications and limitations of these findings in the context of recent studies of dense hydrogen under similar conditions.

physics.comp-ph↗

arXiv2025

Super Co-alignment of Human and AI for Sustainable Symbiotic Society

As Artificial Intelligence (AI) advances toward Artificial General Intelligence (AGI) and eventually Artificial Superintelligence (ASI), it may potentially surpass human control, deviate from human values, and even lead to irreversible catastrophic consequences in extreme cases. This looming risk underscores the critical importance of the "superalignment" problem - ensuring that AI systems which are much smarter than humans, remain aligned with human (compatible) intentions and values. While current scalable oversight and weak-to-strong generalization methods demonstrate certain applicability, they exhibit fundamental flaws in addressing the superalignment paradigm - notably, the unidirectional imposition of human values cannot accommodate superintelligence's autonomy or ensure AGI/ASI's stable learning. We contend that the values for sustainable symbiotic society should be co-shaped by humans and living AI together, achieving "Super Co-alignment." Guided by this vision, we propose a concrete framework that integrates external oversight and intrinsic proactive alignment. External oversight superalignment should be grounded in human-centered ultimate decision, supplemented by interpretable automated evaluation and correction, to achieve continuous alignment with humanity's evolving values. Intrinsic proactive superalignment is rooted in a profound understanding of the Self, others, and society, integrating self-awareness, self-reflection, and empathy to spontaneously infer human intentions, distinguishing good from evil and proactively prioritizing human well-being. The integration of externally-driven oversight with intrinsically-driven proactive alignment will co-shape symbiotic values and rules through iterative human-ASI co-alignment, paving the way for achieving safe and beneficial AGI and ASI for good, for human, and for a symbiotic ecology.

cs.AI↗

arXiv2025

Feature Hallucination for Self-supervised Action Recognition

Understanding human actions in videos requires more than raw pixel analysis; it relies on high-level semantic reasoning and effective integration of multimodal features. We propose a deep translational action recognition framework that enhances recognition accuracy by jointly predicting action concepts and auxiliary features from RGB video frames. At test time, hallucination streams infer missing cues, enriching feature representations without increasing computational overhead. To focus on action-relevant regions beyond raw pixels, we introduce two novel domain-specific descriptors. Object Detection Features (ODF) aggregate outputs from multiple object detectors to capture contextual cues, while Saliency Detection Features (SDF) highlight spatial and intensity patterns crucial for action recognition. Our framework seamlessly integrates these descriptors with auxiliary modalities such as optical flow, Improved Dense Trajectories, skeleton data, and audio cues. It remains compatible with state-of-the-art architectures, including I3D, AssembleNet, Video Transformer Network, FASTER, and recent models like VideoMAE V2 and InternVideo2. To handle uncertainty in auxiliary features, we incorporate aleatoric uncertainty modeling in the hallucination step and introduce a robust loss function to mitigate feature noise. Our multimodal self-supervised action recognition framework achieves state-of-the-art performance on multiple benchmarks, including Kinetics-400, Kinetics-600, and Something-Something V2, demonstrating its effectiveness in capturing fine-grained action dynamics.

cs.CV↗

arXiv2025

DiaLLMs: EHR Enhanced Clinical Conversational System for Clinical Test Recommendation and Diagnosis Prediction

Recent advances in Large Language Models (LLMs) have led to remarkable progresses in medical consultation. However, existing medical LLMs overlook the essential role of Electronic Health Records (EHR) and focus primarily on diagnosis recommendation, limiting their clinical applicability. We propose DiaLLM, the first medical LLM that integrates heterogeneous EHR data into clinically grounded dialogues, enabling clinical test recommendation, result interpretation, and diagnosis prediction to better align with real-world medical practice. To construct clinically grounded dialogues from EHR, we design a Clinical Test Reference (CTR) strategy that maps each clinical code to its corresponding description and classifies test results as "normal" or "abnormal". Additionally, DiaLLM employs a reinforcement learning framework for evidence acquisition and automated diagnosis. To handle the large action space, we introduce a reject sampling strategy to reduce redundancy and improve exploration efficiency. Furthermore, a confirmation reward and a class-sensitive diagnosis reward are designed to guide accurate diagnosis prediction. Extensive experimental results demonstrate that DiaLLM outperforms baselines in clinical test recommendation and diagnosis prediction.

cs.AI↗

arXiv2025

A fluctuating lattice Boltzmann method for viscoelastic fluid flows

This study introduces a novel fluctuating lattice Boltzmann (LB) method for simulating viscoelastic fluid flows governed by the Oldroyd-B model. In contrast to conventional LB approaches that explicitly compute the divergence of the polymer stress tensor using finite-difference schemes, the proposed method incorporates the polymer stress implicitly by introducing a polymer stress fluctuation term directly into the evolution equation. This treatment avoids the need for stress-gradient computations, and preserves the physical characteristics of viscoelastic fluid flows. The proposed method is validated against four classical benchmark problems: the simplified four-roll mill, planar Poiseuille flow, unsteady Womersley flow, and the three-dimensional Taylor-Green vortex. The numerical results show excellent agreement with analytical solutions and previous numerical results, confirming the method's reliability in viscoelastic fluid dynamics. Moreover, performance evaluations demonstrate that the present model reduces the memory occupancy and enhances computational efficiency, highlighting its potential for large-scale simulations of complex viscoelastic flows systems.

physics.flu-dyn↗

arXiv2025

Fubini-Study forms on punctured Riemann surfaces

In this paper we consider a punctured Riemann surface endowed with a Hermitian metric that equals the Poincaré metric near the punctures, and a holomorphic line bundle that polarizes the metric. We show that the quotient of the induced Fubini-Study forms by Kodaira maps of high tensor powers of the line bundle and the Poincaré form near the singularity grows polynomially uniformly on a neighborhood of the singularity as the tensor power tends to infinity, as an application of the method in [5].

math.CV↗

arXiv2025

Exciton condensation of composite fermions in double layer quantum Hall systems

We study fractional quantum Hall states in double layer systems that can be interpreted as exciton condensates of composite fermions. An electron in one layer is dressed by two fluxes from the same layer and two fluxes from the other layer to become composite fermions that form effective Landau levels. It is found that two types of composite fermion exciton condensates could occur. In the first type ones, all effective levels are partially occupied and excitonic correlations are present between composite fermions in the same effective level. In the second type ones, composite fermions in the topmost effective levels of the two layers form exciton condensate whereas those in lower effective levels are independent. The electric transport signatures of these states are analyzed. We demonstrate using numerical calculations that some composite fermion exciton condensates can be realized in microscopic models that are relevant for graphene and transition metal dichalcogenides. For a fixed total filling factor, an exciton condensate may only be realized when the electron densities in the two layers belong to a certain range. It is possible that two types of states appear at the same total filling factor in different ranges. These results shed light on recent experimental observations and also suggest some promising future directions.

cond-mat.str-el↗

arXiv2025

E2MPL:An Enduring and Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation

Few-shot unsupervised domain adaptation (FS-UDA) leverages a limited amount of labeled data from a source domain to enable accurate classification in an unlabeled target domain. Despite recent advancements, current approaches of FS-UDA continue to confront a major challenge: models often demonstrate instability when adapted to new FS-UDA tasks and necessitate considerable time investment. To address these challenges, we put forward a novel framework called Enduring and Efficient Meta-Prompt Learning (E2MPL) for FS-UDA. Within this framework, we utilize the pre-trained CLIP model as the backbone of feature learning. Firstly, we design domain-shared prompts, consisting of virtual tokens, which primarily capture meta-knowledge from a wide range of meta-tasks to mitigate the domain gaps. Secondly, we develop a task prompt learning network that adaptively learns task-specific specific prompts with the goal of achieving fast and stable task generalization. Thirdly, we formulate the meta-prompt learning process as a bilevel optimization problem, consisting of (outer) meta-prompt learner and (inner) task-specific classifier and domain adapter. Also, the inner objective of each meta-task has the closed-form solution, which enables efficient prompt learning and adaptation to new tasks in a single step. Extensive experimental studies demonstrate the promising performance of our framework in a domain adaptation benchmark dataset DomainNet. Compared with state-of-the-art methods, our method has improved accuracy by at least 15.4% and reduced the time by 68.5% on average in 5-way 1-shot tasks, and improved accuracy by 8.7% and reduced the time by 74.1% on average in 5-way 5-shot tasks. Moreover, our approach exhibits more enduring performance than the other methods, i.e., being more stable across 3600 test tasks.

cs.CV↗

arXiv2025

A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce

Reinforcement learning (RL) has become a prevailing approach for fine-tuning large language models (LLMs) on complex reasoning tasks. Among recent methods, GRPO stands out for its empirical success in training models such as DeepSeek-R1, yet the sources of its effectiveness remain poorly understood. In this work, we revisit GRPO from a reinforce-like algorithm perspective and analyze its core components. Surprisingly, we find that a simple rejection sampling baseline, RAFT, which trains only on positively rewarded samples, yields competitive performance than GRPO and PPO. Our ablation studies reveal that GRPO's main advantage arises from discarding prompts with entirely incorrect responses, rather than from its reward normalization. Motivated by this insight, we propose Reinforce-Rej, a minimal extension of policy gradient that filters both entirely incorrect and entirely correct samples. Reinforce-Rej improves KL efficiency and stability, serving as a lightweight yet effective alternative to more complex RL algorithms. We advocate RAFT as a robust and interpretable baseline, and suggest that future advances should focus on more principled designs for incorporating negative samples, rather than relying on them indiscriminately. Our findings provide guidance for future work in reward-based LLM post-training.

cs.LG↗

arXiv2025

Charged-current quasielastic neutrino scattering off nuclei with nucleon-nucleon short-range correlations

In recent years, many studies on neutrino-nucleus scattering have been carried out to investigate nuclear structures and the interactions between neutrinos and nucleons. This paper develops a charged-current quasielastic (CCQE) neutrino-nucleus scattering model to explore the nuclear mean-field dynamics and short-range correlation effects. In this model, the nuclear structure effect is depicted using the scaling function, while the neutrinonucleon interaction is represented by the elementary weak cross section. Results indicate that the double-differential cross section of scattered muon is influenced by the energy and momentum of nucleon in nuclei, and the total cross section depends primarily on the incident neutrino energy. Furthermore, incorporating short-range correlations yields the flux-integrated differential cross sections at high-T region producing larger values, a longer tail, and achieving better experimental consistency. It eventually elucidates the physical relationship between the neutrinonucleus scattering cross section and the variation in incident neutrino energy. The studies in this paper furnishes insights for the research of nucleon dynamics and provides detailed examinations of the neutrino-nucleus scattering mechanism.

nucl-th↗

arXiv2025

ChartReasoner: Code-Driven Modality Bridging for Long-Chain Reasoning in Chart Question Answering

Recently, large language models have shown remarkable reasoning capabilities through long-chain reasoning before responding. However, how to extend this capability to visual reasoning tasks remains an open challenge. Existing multimodal reasoning approaches transfer such visual reasoning task into textual reasoning task via several image-to-text conversions, which often lose critical structural and semantic information embedded in visualizations, especially for tasks like chart question answering that require a large amount of visual details. To bridge this gap, we propose ChartReasoner, a code-driven novel two-stage framework designed to enable precise, interpretable reasoning over charts. We first train a high-fidelity model to convert diverse chart images into structured ECharts codes, preserving both layout and data semantics as lossless as possible. Then, we design a general chart reasoning data synthesis pipeline, which leverages this pretrained transport model to automatically and scalably generate chart reasoning trajectories and utilizes a code validator to filter out low-quality samples. Finally, we train the final multimodal model using a combination of supervised fine-tuning and reinforcement learning on our synthesized chart reasoning dataset and experimental results on four public benchmarks clearly demonstrate the effectiveness of our proposed ChartReasoner. It can preserve the original details of the charts as much as possible and perform comparably with state-of-the-art open-source models while using fewer parameters, approaching the performance of proprietary systems like GPT-4o in out-of-domain settings.

cs.CL↗

arXiv2025

Visible Brillouin-quadratic microlaser in a high-Q thin-film lithium niobate microdisk

Narrow-linewidth lasers at short/visible wavelengths are crucial for quantum and atomic applications, such as atomic clocks, quantum computing, atomic and molecular spectroscopy, and quantum sensing. However, such lasers are often only accessible in bulky tabletop systems and remain scarce in integrated photonic platform. Here, we report an on-chip visible Brillouin-quadratic microlaser in a 117-um-diameter thin-film lithium niobate (TFLN) microdisk via dispersion engineering. Enabled by the ultra-high Q factor of 4.0X10(6) and small mode volume, strong photon-phonon interaction and high second-order nonlinearity of the TFLN microdisk, narrow-linewidth Stokes Brillouin lasing (SBL) is demonstrated with 10.17 GHz Brillouin shift under a 1560-nm pump, exhibiting a short-term narrow linewidth of 254 Hz and a low threshold of only 1.81 mW. Meanwhile, efficient second harmonic generation (SHG) of the SBL signal is also observed at 780 nm, with a normalized conversion efficiency of 3.61%/mW, made possible by simultaneous phase matching fulfillments for both narrow-linewidth SBL and its SHG. This demonstration of an integrated ultra-narrow linewidth visible wavelength Brillouin-quadratic lasers opens new avenues toward chip-scale quantum information processing and precise metrology.

physics.optics↗

arXiv2025

SeerAttention-R: Sparse Attention Adaptation for Long Reasoning

We introduce SeerAttention-R, a sparse attention framework specifically tailored for the long decoding of reasoning models. Extended from SeerAttention, SeerAttention-R retains the design of learning attention sparsity through a self-distilled gating mechanism, while removing query pooling to accommodate auto-regressive decoding. With a lightweight plug-in gating, SeerAttention-R is flexible and can be easily integrated into existing pretrained model without modifying the original parameters. We demonstrate that SeerAttention-R, trained on just 0.4B tokens, maintains near-lossless reasoning accuracy with 4K token budget in AIME benchmark under large sparse attention block sizes (64/128). Using TileLang, we develop a highly optimized sparse decoding kernel that achieves near-theoretical speedups of up to 9x over FlashAttention-3 on H100 GPU at 90% sparsity. Code is available at: https://github.com/microsoft/SeerAttention.

cs.LG↗

arXiv2025

Modern applications of machine learning in quantum sciences

In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.

quant-ph↗

arXiv2025

ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering

Chart question answering (CQA) has become a critical multimodal task for evaluating the reasoning capabilities of vision-language models. While early approaches have shown promising performance by focusing on visual features or leveraging large-scale pre-training, most existing evaluations rely on rigid output formats and objective metrics, thus ignoring the complex, real-world demands of practical chart analysis. In this paper, we introduce ChartMind, a new benchmark designed for complex CQA tasks in real-world settings. ChartMind covers seven task categories, incorporates multilingual contexts, supports open-domain textual outputs, and accommodates diverse chart formats, bridging the gap between real-world applications and traditional academic benchmarks. Furthermore, we propose a context-aware yet model-agnostic framework, ChartLLM, that focuses on extracting key contextual elements, reducing noise, and enhancing the reasoning accuracy of multimodal large language models. Extensive evaluations on ChartMind and three representative public benchmarks with 14 mainstream multimodal models show our framework significantly outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought, highlighting the importance of flexible chart understanding for real-world CQA. These findings suggest new directions for developing more robust chart reasoning in future research.

cs.CL↗