arXiv subjects

Zhenyu Wang

Publications and source records attributed to Zhenyu Wang.

At least 73 records · Page 4Linked to original sources

arXiv2025

LoopExpose: An Unsupervised Framework for Arbitrary-Length Exposure Correction

Exposure correction is essential for enhancing image quality under challenging lighting conditions. While supervised learning has achieved significant progress in this area, it relies heavily on large-scale labeled datasets, which are difficult to obtain in practical scenarios. To address this limitation, we propose a pseudo label-based unsupervised method called LoopExpose for arbitrary-length exposure correction. A nested loop optimization strategy is proposed to address the exposure correction problem, where the correction model and pseudo-supervised information are jointly optimized in a two-level framework. Specifically, the upper-level trains a correction model using pseudo-labels generated through multi-exposure fusion at the lower level. A feedback mechanism is introduced where corrected images are fed back into the fusion process to refine the pseudo-labels, creating a self-reinforcing learning loop. Considering the dominant role of luminance calibration in exposure correction, a Luminance Ranking Loss is introduced to leverage the relative luminance ordering across the input sequence as a self-supervised constraint. Extensive experiments on different benchmark datasets demonstrate that LoopExpose achieves superior exposure correction and fusion performance, outperforming existing state-of-the-art unsupervised methods. Code is available at https://github.com/FALALAS/LoopExpose.

cs.CV↗

arXiv2025

Machine learning-driven elasticity prediction in advanced inorganic materials via convolutional neural networks

Inorganic crystal materials have broad application potential due to excellent physical and chemical properties, with elastic properties (shear modulus, bulk modulus) crucial for predicting materials' electrical conductivity, thermal conductivity and mechanical properties. Traditional experimental measurement suffers from high cost and low efficiency, while theoretical simulation and graph neural network-based machine learning methods--especially crystal graph convolutional neural networks (CGCNNs)--have become effective alternatives, achieving remarkable results in predicting material elastic properties. This study trained two CGCNN models using shear modulus and bulk modulus data of 10987 materials from the Matbench v0.1 dataset, which exhibit high accuracy (mean absolute error <13, coefficient of determination R-squared close to 1) and good generalization ability. Materials were screened to retain those with band gaps between 0.1-3.0 eV and exclude radioactive element-containing compounds. The final predicted dataset comprises two parts: 54359 crystal structures from the Materials Project database and 26305 crystal structures discovered by Merchant et al. (2023 Nature 624 80). Ultimately, this study completed the prediction of shear modulus and bulk modulus for 80664 inorganic crystals. This work enriches existing material elastic data resources and provides robust support for material design, with all data openly available at https://doi.org/10.57760/sciencedb.j00213.00104.

cond-mat.mtrl-sci↗

arXiv2025

Intelligent Computing Social Modeling and Methodological Innovations in Political Science in the Era of Large Language Models

The recent wave of artificial intelligence, epitomized by large language models (LLMs),has presented opportunities and challenges for methodological innovation in political science,sparking discussions on a potential paradigm shift in the social sciences. However, how can weunderstand the impact of LLMs on knowledge production and paradigm transformation in thesocial sciences from a comprehensive perspective that integrates technology and methodology? What are LLMs' specific applications and representative innovative methods in political scienceresearch? These questions, particularly from a practical methodological standpoint, remainunderexplored. This paper proposes the "Intelligent Computing Social Modeling" (ICSM) methodto address these issues by clarifying the critical mechanisms of LLMs. ICSM leverages thestrengths of LLMs in idea synthesis and action simulation, advancing intellectual exploration inpolitical science through "simulated social construction" and "simulation validation." Bysimulating the U.S. presidential election, this study empirically demonstrates the operationalpathways and methodological advantages of ICSM. By integrating traditional social scienceparadigms, ICSM not only enhances the quantitative paradigm's capability to apply big data toassess the impact of factors but also provides qualitative paradigms with evidence for socialmechanism discovery at the individual level, offering a powerful tool that balances interpretabilityand predictability in social science research. The findings suggest that LLMs will drivemethodological innovation in political science through integration and improvement rather thandirect substitution.

cs.CY↗

arXiv2025

SN 2024gy: Multi-epoch Spectroscopic Features Suggestive of Delayed Detonation in a Type Ia Supernova

We present photometric and spectroscopic observations of SN 2024gy, a Type Ia supernova (SN Ia) exhibiting high-velocity features (HVFs) in its early-time spectra. This SN reaches a peak $B$-band magnitude of $-19.25 \pm 0.29$ mag and subsequently declines by $Δm_{15}(B) \approx 1.12$ mag, consistent with the luminosity-width relation characteristic of normal SNe Ia. Based on the peak thermal luminosity of $(1.2 \pm 0.3) \times 10^{43}$ erg s$^{-1}$, we estimate that $0.57 \pm 0.14~\rm M_{\odot}$ of $^{56}$Ni was synthesized during the explosion. Our dense early spectral monitoring revealed significant velocity disparities within the ejecta. Notably, absorption features from the Ca II near-infrared triplet were observed at velocities exceeding 25,000 km s$^{-1}$, while the Si II $λ$6355 line velocity at the same epoch was significantly lower at $\sim$ 16,000 km s$^{-1}$. This velocity disparity likely reflects distinct ionization states of intermediate-mass elements in the outermost layers. The prominent Ca II HVFs may originate from ionization suppression within the highest-velocity ejecta, potentially indicative of minimal hydrogen mixing in a delayed-detonation explosion scenario. Additionally, the Ni/Fe ratio derived from the nebular spectrum of SN 2024gy provides further support for this model.

astro-ph.HE↗

arXiv2025

SN 2024iss: A Double-peaked Type IIb Supernova with Evidence of Circumstellar Interaction

We present optical, ultraviolet, and X-ray observations of supernova (SN) 2024iss, a Type IIb SN that shows a prominent double-peaked light curve. We modeled the first peak with a semianalytical shock-cooling model and the X-ray emission with a free-free model. We compare the envelope radius and mass-loss rate with other Type IIb SNe to explore the relationships between the progenitor envelope and the circumstellar material (CSM). The shock-cooling peak in the $V$-band light curve reached $M_V = -17.33\pm 0.26$mag, while the $^{56}$Ni-powered second peak attained $M_V = -17.43\pm 0.26$mag. Early spectra show an photospheric velocity of $\sim19,400\,km\,s^{-1}$ at 3.82days from the H$α$ P~Cygni profile. The Balmer lines persist at least +87 days after the explosion, characterizing hydrogen-rich ejecta. Modeling the first light-curve peak suggests an extended envelope with a mass of $0.11\pm0.04\,M_{\odot}$ and a radius of $244\pm43~R_{\odot}$. Fitting the second light-curve peak with an Arnett-like model indicates a typical $^{56}$Ni mass of $ 0.117\pm0.013~M_{\odot}$ and a relatively low ejecta mass of $1.272\pm0.343\,M_{\odot}$. X-ray observations reveal bright thermal bremsstrahlung emission and indicate a mass-loss rate of $1.6\times10^{-5}\ M_{\odot} \ \rm{yr}^{-1}$. SN 2024iss occupies a transitional position between the two subclasses of extended (eIIb) and compact (cIIb) Type IIb SNe. Its envelope radius and pre-explosion mass-loss rate appear to be correlated as theoretically predicted. The observational properties of SN 2024iss are compatible with a binary interaction scenario being the dominant mechanism for envelope stripping. Furthermore, the low column density of neutral hydrogen suggests a compact CSM with an outer radius of $\lesssim1.3\times10^{14}$ cm, indicating that the progenitor star experienced eruptive mass loss within $\sim4\,yr$ of its terminal explosion.

astro-ph.HE↗

arXiv2025

Computing Solvation Shell Dynamics and Energetics in Electron Transfer Reactions via Molecular Dynamics Simulations

Marcus theory is fundamental to describing electron transfer reactions and quantifying their rates, effectively representing the energy surface associated with an electron transfer from the reactant to the product ionic state via parabolas within a reaction coordinate diagram. Here, we present an intuitive and computationally efficient generalised reaction coordinate amenable to molecular dynamics simulations. By utilising the nuclear charge of the ion, we are able to quantify in a targeted approach changes in the ion's solvation shell, thereby efficiently obtaining the free energy profile associated with the electron transfer, the transition state geometry and the evolution of the water network.

physics.chem-ph↗

arXiv2025

Optimization research for rescue hot standby EMU location and coverage area in a large-scale high-speed railway network

With the extension of China high-speed railway network, the number of railway stations is increasing. The challenge that railway companies are facing is how to reasonably plan the location of hot standby Electric Multiple Units (EMU) and determine the rescue coverage area of each hot standby EMU. It is uneconomical to configure a hot standby EMU for each station. Railway companies want to use the minimum number of hot standby EMUs to provide rescue tasks for the entire high-speed railway network. In this paper, we develop the optimization models for hot standby EMU location and coverage area, respectively. The models aim to maximize the rescue distance and minimize the number of hot standby EMUs. Wha'ts more, in order to reduce the complexity of the models, a method of merging railway lines is used to transform the "point-to-network" to "point-to-point" rescue problem. Two numerical examples are carried to demonstrate the effectiveness of the models. The developed models are linear, and we use Python 3.7 to call Gurobi 9.5.2 to solve the models. In the results, we can find that the developed models can provide an optimal and reasonable plan for the location and coverage area of hot standby EMUs.

cs.OH↗

arXiv2025

Towards 3D Objectness Learning in an Open World

Recent advancements in 3D object detection and novel category detection have made significant progress, yet research on learning generalized 3D objectness remains insufficient. In this paper, we delve into learning open-world 3D objectness, which focuses on detecting all objects in a 3D scene, including novel objects unseen during training. Traditional closed-set 3D detectors struggle to generalize to open-world scenarios, while directly incorporating 3D open-vocabulary models for open-world ability struggles with vocabulary expansion and semantic overlap. To achieve generalized 3D object discovery, We propose OP3Det, a class-agnostic Open-World Prompt-free 3D Detector to detect any objects within 3D scenes without relying on hand-crafted text prompts. We introduce the strong generalization and zero-shot capabilities of 2D foundation models, utilizing both 2D semantic priors and 3D geometric priors for class-agnostic proposals to broaden 3D object discovery. Then, by integrating complementary information from point cloud and RGB image in the cross-modal mixture of experts, OP3Det dynamically routes uni-modal and multi-modal features to learn generalized 3D objectness. Extensive experiments demonstrate the extraordinary performance of OP3Det, which significantly surpasses existing open-world 3D detectors by up to 16.0% in AR and achieves a 13.5% improvement compared to closed-world 3D detectors.

cs.CV↗

arXiv2025

mmJoints: Expanding Joint Representations Beyond (x,y,z) in mmWave-Based 3D Pose Estimation

In mmWave-based pose estimation, sparse signals and weak reflections often cause models to infer body joints from statistical priors rather than sensor data. While prior knowledge helps in learning meaningful representations, over-reliance on it degrades performance in downstream tasks like gesture and activity recognition. In this paper, we introduce mmJoints, a framework that augments a pre-trained, black-box mmWave-based 3D pose estimator's output with additional joint descriptors. Rather than mitigating bias, mmJoints makes it explicit by estimating the likelihood of a joint being sensed and the reliability of its predicted location. These descriptors enhance interpretability and improve downstream task accuracy. Through extensive evaluations using over 115,000 signal frames across 13 pose estimation settings, we show that mmJoints estimates descriptors with an error rate below 4.2%. mmJoints also improves joint position accuracy by up to 12.5% and boosts activity recognition by up to 16% over state-of-the-art methods.

cs.CV↗

arXiv2025

Domain wall skyrmion-based magnonic crystal

Magnonic waveguide based on domain wall (DW) is considered as a crucial breakthrough toward the realization of magnonic nanocircuits. However, the effective control of spin waves propagating in DWs remains to be explored. Here, we construct a magnonic crystal (MC) by using a chain of the domain wall skyrmions (DWSKs) to manipulate the spin-wave propagation in DWs. We show that the DWSK chain can be created by leveraging voltage-controlled Dzyaloshinskii-Moriya interaction. The DWSK-based MC opens magnonic bandgaps, which can be dynamically adjusted through magnetic fields modulating the DWSK size. Furthermore, the manipulation of spin waves by the DWSK-based MC maintains robust in curved DW, demonstrating its adaptability to complex device architectures. Our work provides an effective method to control the spin-wave propagation in DWs and paves the way for designing energy-efficient magnonic nanocircuits.

cond-mat.mes-hall↗

arXiv2025

Electronic-correlation-assisted charge stripe order in a Kagome superconductor

A central mystery in high-temperature cuprate superconductors is the coexistence of multiple exotic orders, which is presumably associated with strong electronic correlation. The ongoing interest in this enigmatic phenomenon is further energized when similar electronic orders and states emerge and coexist in less correlated Kagome superconductors. Here, by utilizing angle-resolved photoemission spectroscopy (ARPES), nuclear magnetic resonance (NMR) spectroscopy, scanning tunneling microscopy (STM) and first-principles calculations, we reveal the sudden emergence of a distinct short-range charge stripe order in Sn-doped CsV$_3$Sb$_5$ Kagome superconductors when the long-range $2 \times 2$ charge density wave order in pristine CsV$_3$Sb$_5$ is suppressed. This short-range stripe order features a modulation vector of approximately 1/3 along one of the three lattice directions, induces remarkable quasiparticle scattering between the original quasi-1D Kagome-d-bands and their replica of folding, and clearly suppresses the electron density of states at the Fermi level. Our first-principles calculations reveal that $3 \times 1$ supermodulation represents a hidden secondary instability in pristine CsV$_3$Sb$_5$. This instability is further enhanced by in-plane chemical pressure induced by Sn substitution, and coupled to the electronic correlation, leading to a unique charge stripe order in the system. As such, our results reveal a new route toward emergent electronic orders via cooperative interactions between the lattice and electronic degrees of freedom.

cond-mat.supr-con↗

arXiv2025

Smart Interrupted Routing Based on Multi-head Attention Mask Mechanism-Driven MARL in Software-defined UASNs

Routing-driven timely data collection in Underwater Acoustic Sensor Networks (UASNs) is crucial for marine environmental monitoring, disaster warning and underwater resource exploration, etc. However, harsh underwater conditions, including high delays, limited bandwidth, and dynamic topologies - make efficient routing decisions challenging in UASNs. In this paper, we propose a smart interrupted routing scheme for UASNs to address dynamic underwater challenges. We first model underwater noise influences from real underwater routing features, e.g., turbulence and storms. We then propose a Software-Defined Networking (SDN)-based Interrupted Software-defined UASNs Reinforcement Learning (ISURL) framework which ensures adaptive routing through dynamically failure handling (e.g., energy depletion of sensor nodes or link instability) and real-time interrupted recovery. Based on ISURL, we propose MA-MAPPO algorithm, integrating multi-head attention mask mechanism with MAPPO to filter out infeasible actions and streamline training. Furthermore, to support interrupted data routing in UASNs, we introduce MA-MAPPO_i, MA-MAPPO with interrupted policy, to enable smart interrupted routing decision in UASNs. The evaluations demonstrate that our proposed routing scheme achieves exact underwater data routing decision with faster convergence speed and lower routing delays than existing approaches.

cs.NI↗

arXiv2025

OpenCSP: A Deep Learning Framework for Crystal Structure Prediction from Ambient to High Pressure

High-pressure crystal structure prediction (CSP) underpins advances in condensed matter physics, planetary science, and materials discovery. Yet, most large atomistic models are trained on near-ambient, equilibrium data, leading to degraded stress accuracy at tens to hundreds of gigapascals and sparse coverage of pressure-stabilized stoichiometries and dense coordination motifs. Here, we introduce OpenCSP, a machine learning framework for CSP tasks spanning ambient to high-pressure conditions. This framework comprises an open-source pressure-resolved dataset alongside a suite of publicly available atomistic models that are jointly optimized for accuracy in energy, force, and stress predictions. The dataset is constructed via randomized high-pressure sampling and iteratively refined through an uncertainty-guided concurrent learning strategy, which enriches underrepresented compression regimes while suppressing redundant DFT labeling. Despite employing a training corpus one to two orders of magnitude smaller than those of leading large models, OpenCSP achieves comparable or superior performance in high-pressure enthalpy ranking and stability prediction. Across benchmark CSP tasks spanning a wide pressure window, our models match or surpass MACE-MPA-0, MatterSim v1 5M, and GRACE-2L-OAM, with the largest gains observed at elevated pressures. These results demonstrate that targeted, pressure-aware data acquisition coupled with scalable architectures enables data-efficient, high-fidelity CSP, paving the way for autonomous materials discovery under ambient and extreme conditions.

cond-mat.mtrl-sci↗

arXiv2025

CoMET: A Contrastive-Masked Brain Foundation Model for Universal EEG Representation

Electroencephalography (EEG) is a non-invasive technique for recording brain activity, widely used in brain-computer interfaces, clinic, and healthcare. Traditional EEG deep models typically focus on specific dataset and task, limiting model size and generalization. Recently, self-supervised brain foundation models have emerged and been applied to various downstream tasks. Nevertheless, these models still have limitations: current SOTA models typically rely on masked reconstruction strategy; however, EEG features of adjacent channels are highly correlated, which causes the pre-training to overly focus on low-dimensional signal-similarity features in local regions and neglect the global discriminative patterns vital for downstream tasks. To address these limitations, we propose a brain foundation model called CoMET. Specifically, we employ the masked autoencoder with redesigned patching and embedding for EEG as backbone and devise a novel contrastive learning framework with mirror-scale augmentation to strengthen the global discrimination ability. CoMET is pre-trained on mixed EEG datasets over 3000 subjects with over one million samples. It is evaluated on ten different downstream datasets, and the SOTA results demonstrate CoMET's superior ability in extracting universal EEG representations and strong clinical potential.

eess.SP↗

arXiv2025

Dimensional hierarchy of topological bound states in the continuum

Bound states in the continuum (BICs), with the ability of trapping and manipulating waves within the radiation continuum, have gained significant attention for their potential applications in optics and acoustics. However, challenges arise in reducing wave leakage and noise from fabrication imperfections. The emergence of robust wave manipulations based on topological BICs (TBICs) offers promising solutions. Traditionally, TBICs of different dimensions are observed separately in distinct systems. Here, we report the experimental discovery of the coexistence of two-dimensional surface TBICs and one-dimensional hinge TBICs in a single three-dimensional phononic crystal system. Such an unprecedented dimensional hierarchy of TBICs is triggered by the mechanism of separability and protected by the valley Chern numbers. Notably, these TBICs inherit dispersive propagation characteristics from valley topology and can propagate robustly against defects without leakage. Our findings offer an efficient approach to multidimensional TBICs and can be applied in designing highly efficient acoustic devices for wave trapping and manipulation in multidimensional environments.

cond-mat.mtrl-sci↗

arXiv2025

Unveiling the landscape of Mottness and its proximity to superconductivity in 4Hb-TaS$_2$

Mott physics is at the root of a plethora of many-body quantum phenomena in quantum materials. Recently, the stacked or twisted structures of van der Waals (vdW) materials have emerged as a unique platform for realizing exotic correlated states in the vicinity of the Mott transition. However, the definitive feature of Mottness and how it rules the low-energy electronic state remain elusive and experimentally inaccessible in many interesting regimes. Here, we quantitatively describe a filling-controlled Mott state and its interplay with superconductivity by scanning tunnelling spectroscopy in a vdW bulk heterostructure, 4Hb-TaS$_2$, that interleaves strongly correlated 1T-TaS$_2$ layers with superconducting 1H-Ta$_2$ layers. The fine tunability of electron doping induced by interlayer charge transfer allows us to continuously track the spectral function with unsurpassed energy resolution from a depleted narrow band (0.2 electrons per site) toward a Mott transition at half filling. The gradually emerging Mott-Hubbard bands, followed by the sharpening and vanishing of the central quasiparticle peak as predicted in the Brinkman-Rice scenario, unambiguously demonstrate the Mott physics at play. Importantly, the renormalization of the low-energy electrons acts destructively on the superconducting pairing potential, leaving behind nonsuperconducting, paramagnetic puddles at the nanoscale. Our results reveal a seminal system near the border of the Mott criterion that enables us to illustrate the predictive power of the Hubbard model, and set such heterostructures as promising ground for realizing new correlated states in the heavily doped Mott regime.

cond-mat.supr-con↗

arXiv2025

Anomalous narrow-band correlation in a natural superconducting heterostructure

A new frontier in condensed matter physics is to stack atomically thin layered-materials with different properties and create intriguing phenomena which do not exist in any of the constituent layers. Transition metal dichalcogenide 4Hb-TaS$_2$, with an alternating stacking of a spin liquid candidate 1T-TaS$_2$ and a superconductor 1H-TaS$_2$, is a natural heterostructure for such a purpose. Recently, rare phenomena are indeed observed, including chiral superconductivity, two-component nematic superconductivity, topological surface superconductivity and enigmatic magnetic memory. A widely proposed starting point to understand such a mysterious heterostructure requires strong electronic correlation, presumably provided by 1T-TaS$_2$ layers with a narrow flat band near the Fermi level ($E_F$). Here, by using angle-resolved photoemission spectroscopy, we reveal the theoretically expected flat band near $E_F$ in the energy-momentum space for the first time. However, this flat band only exists on the 1T-TaS$_2$ terminated surface layer with broken translational symmetry, but not on the 1T-TaS$_2$ layers buried in the bulk. These results directly challenge the foundation of the current theoretical paradigm. On the 1T-TaS$_2$ terminated surface layer, we further reveal a pseudogap and an anomalous doping effect. These phenomena and the dichotomy between surface and bulk layers also shed new light on the unusual coexistence of distinct electronic orders in this mysterious heterostructure.

cond-mat.supr-con↗

arXiv2025

Transport Evidence for Wigner Crystals in Monolayer MoTe2

The crystallization of charge carriers, dubbed the Wigner crystal, is anticipated at low densities in clean two-dimensional electronic systems (2DES). While there has been extensive investigation across diverse platforms, probing spontaneous charge and spin ordering is hindered by disorder effects and limited interaction energies. Here, we report transport evidence for Wigner crystals with antiferromagnetic exchange interactions in high-quality, hexagonal boron nitride encapsulated monolayer MoTe2, a system that achieves a large interaction parameter (r_s) at proper hole densities. A density-tuned metal-insulator transition (MIT) occurring at 3.1E10^11 cm-2 (corresponding to r_s~32) and pronounced nonlinear charge transport in the insulating regime at low temperatures signify the formation of Wigner crystals. Thermal melting of the crystalline phase is observed below approximately 2 K via temperature-dependent nonlinear transport. Magnetoresistance measurements further reveal a substantial enhancement of spin susceptibility as approaching the MIT. The temperature dependence of spin susceptibility in the Wigner crystal phase closely follows the Curie-Weiss law, with the extracted negative Weiss constant illustrating antiferromagnetic exchange interactions. Furthermore, we have found the system exhibits metallic-like differential resistivity under finite DC bias, possibly indicating the existence of a non-equilibrium coherent state in the depinning of Wigner crystals. Our observations establish monolayer MoTe2 as a promising platform for exploring magnetic and dynamic properties of Wigner crystals.

cond-mat.str-el↗