arXiv subjects

Yan Sun

Publications and source records attributed to Yan Sun.

At least 91 records · Page 5Linked to original sources

arXiv2025

CU-JADE: A Method for Traversing Extinction Jumps along the Line of Sight

Although interstellar dust extinction serves as a powerful distance estimator, the solar system's location within the Galactic plane complicates distance determinations, especially for molecular clouds (MCs) at varying distances along the line of sight (LoS). The presence of complex extinction patterns along the LoS introduces degeneracies, resulting in less accurate distance measurements to overlapping MCs in crowded regions of the Galactic plane. In this study, we develop the CUSUM-based Jump-point Analysis for Distance Estimation (CU-JADE), a novel method designed to help mitigate these observational challenges. The key strengths of CU-JADE include: (1) sensitivity to detect abrupt jumps in Distance-$A_λ$ ($D$-$A$) datasets, (2) minimal systematic errors as demonstrated on both mock and observed data, and (3) the ability to combine CUSUM analysis with multiwavelength data to improve the completeness of distance measurements for nearby gas structures, even for extinction values as low as $ΔA_{V} \gtrsim 0.15$ mag. By combining CO survey data with a large sample of stars characterized by high-precision parallaxes and extinctions, we uncovered the multilayered molecular gas distribution in the high-latitude Cepheus region. We also determined accurate distances to MCs beyond the Cygnus Rift by analyzing the intricate structure of gas and extinction within the Galactic plane. Additionally, we constructed a full-sky 3D extinction map extending to 4 kpc, which provides critical insights into dense interstellar medium components dominated by molecular hydrogen. These results advance our understanding of the spatial distribution and physical properties of MCs across the Milky Way.

astro-ph.GA↗

arXiv2025

Molecular Gas Distribution toward the Inner and Outer Galaxy Revealed by MWISP -- the Galactic Longitude 45°--60°and 120°--130°

Molecular clouds (MCs) are cradles of star and planet formation, thereby playing an important role in the evolution of galaxies. Based on the unbiased Milky Way Imaging Scroll Painting (MWISP) survey data of $^{12}$CO, $^{13}$CO, and C$^{18}$O (J=1--0) line emission in two regions toward the inner and outer Galaxy, i.e. the G50 ($44.75°\le l \le 60.25°$) and G120 ($119.75°\le l \le 130.25°$) regions, the distribution of molecular gas is studied. Both regions have Galactic latitudes of $|b| \le 5.25°$. A catalog containing 24724 MCs is constructed from the data. In our proximity, several molecular structures with large angular scales and small velocity dispersions are discovered, resembling curtains of mist. Beyond the nearby molecular gas, a clear aggregation of MCs along coherent structures in the Galactic plane is visible, sketching spiral arm structures. Nevertheless, the aggregation of MCs is also detected in the inter-arm region between the Perseus and Outer arms in the G50 region. The Galactic molecular disk in this inter-arm region is found to be thinner than that in the adjacent spiral arm region. In addition, the thickness of the Galactic molecular disk examined here is found to be correlated with the warp of it, indicating their homologous origins. The molecular disk has a typical thickness of ~220 pc in the inner Galaxy. Moreover, the dispersion of the MC systemic velocity decreases with increasing galactocentric radius, resulting in lower kinematic distance uncertainties at larger radii. However, the Perseus arm segment in the G120 region exhibits a relatively large cloud-to-cloud velocity dispersion and split components in its MC velocity distribution.

astro-ph.GA↗

arXiv2025

Atomistic mechanisms of phase transitions in all-temperature barocaloric material KPF$_6$

Conventional barocaloric materials typically exhibit limited operating temperature ranges. In contrast, KPF$_6$ has recently been reported to achieve an exceptional all-temperature barocaloric effect (BCE) via pressure-driven phase transitions. Here, we elucidate the atomistic mechanisms underlying the phase transitions through first-principles calculations and machine-learning potential accelerated molecular dynamics simulations. We identify four distinct phases: the room-temperature cubic (C) plastic crystal characterized by strong fluorine orientational disorder (FOD) and anharmonicity, the intermediate-temperature monoclinic (M-II) phase with decreasing FOD, the low-temperature monoclinic (M-I) phase with suppressed FOD, and the fully ordered rhombohedral (R) phase under pressure. Phonon calculations confirm the dynamic stability of the M-II, M-I, and R phases at 0 K, whereas the C phase requires thermal fluctuations for stabilization. Under pressure, all the C, M-II, and M-I phases transform to the R phase, which are driven by cooperative PF$_6$ octahedral rotations coupled with lattice modulations. These pressure-induced phase transitions result in persistent isothermal entropy changes across a wide temperature range, thereby explaining the experimentally observed all-temperature BCE in this material. Hybrid functional calculations reveal wide-bandgap insulating behavior across all phases. This work deciphers the interplay between FOD, anharmonicity, and phase transitions in KPF$_6$, providing important insights for the design of BCE materials with broad operational temperature spans.

cond-mat.mtrl-sci↗

arXiv2025

Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling

Deep learning has revolutionized modern data science. However, how to accurately quantify the uncertainty of predictions from large-scale deep neural networks (DNNs) remains an unresolved issue. To address this issue, we introduce a novel post-processing approach. This approach feeds the output from the last hidden layer of a pre-trained large-scale DNN model into a stochastic neural network (StoNet), then trains the StoNet with a sparse penalty on a validation dataset and constructs prediction intervals for future observations. We establish a theoretical guarantee for the validity of this approach; in particular, the parameter estimation consistency for the sparse StoNet is essential for the success of this approach. Comprehensive experiments demonstrate that the proposed approach can construct honest confidence intervals with shorter interval lengths compared to conformal methods and achieves better calibration compared to other post-hoc calibration techniques. Additionally, we show that the StoNet formulation provides us with a platform to adapt sparse learning theory and methods from linear models to DNNs.

stat.ML↗

arXiv2025

Lunar Orbital VLBI Experiment: motivation, scientific purposes and status

The Lunar Orbital VLBI Experiment (LOVEX) is a scientific component of the Chinese Lunar Exploration Project (CLEP) Chang'E-7. The spaceborne component of LOVEX is implemented onboard the relay satellite QueQiao-2, which was launched on 2024 March 20, and later placed into an elliptical selenocentric orbit. The LOVEX-specific payload consists of an X-band cryogenic receiver, a hydrogen maser frequency standard, and VLBI data formatting and acquisition electronics. Several components of the QueQiao-2 nominal onboard instrumentation, such as the 4.2-meter antenna, the data storage device, and the downlink communication system, contribute to the overall spaceborne VLBI instrumentation. This allows us to form a space radio telescope capable of co-observing with Earth-based radio telescopes in VLBI mode. In this space VLBI system, the length of the baseline extends up to approximately 380,000 km. This paper presents the LOVEX scientific objectives, architecture, instrumentation, pre-launch tests, in-flight verification and calibration, and the first in-flight detections of interferometric response (''fringes'') achieved through observations of the quasar AO 0235+164 and the Chang'E-6 orbital module, positioned at the Sun-Earth Lagrange point L2. These initial results demonstrate the successful performance of LOVEX, verifying its capability for both astronomical and spacecraft tracking observations at ultra-long VLBI baselines.

astro-ph.IM↗

arXiv2025

Towards Robust Sensor-Fusion Ground SLAM: A Comprehensive Benchmark and A Resilient Framework

Considerable advancements have been achieved in SLAM methods tailored for structured environments, yet their robustness under challenging corner cases remains a critical limitation. Although multi-sensor fusion approaches integrating diverse sensors have shown promising performance improvements, the research community faces two key barriers: On one hand, the lack of standardized and configurable benchmarks that systematically evaluate SLAM algorithms under diverse degradation scenarios hinders comprehensive performance assessment. While on the other hand, existing SLAM frameworks primarily focus on fusing a limited set of sensor types, without effectively addressing adaptive sensor selection strategies for varying environmental conditions. To bridge these gaps, we make three key contributions: First, we introduce M3DGR dataset: a sensor-rich benchmark with systematically induced degradation patterns including visual challenge, LiDAR degeneracy, wheel slippage and GNSS denial. Second, we conduct a comprehensive evaluation of forty SLAM systems on M3DGR, providing critical insights into their robustness and limitations under challenging real-world conditions. Third, we develop a resilient modular multi-sensor fusion framework named Ground-Fusion++, which demonstrates robust performance by coupling GNSS, RGB-D, LiDAR, IMU (Inertial Measurement Unit) and wheel odometry. Codes and datasets are publicly available.

cs.RO↗

arXiv2025

Diverse polymorphs and phase transitions in van der Waals In$_2$Se$_3$

Van der Waals In$_2$Se$_3$ has garnered significant attention due to its unique properties and wide applications associated with its rich polymorphs and polymorphic phase transitions. Despite extensive studies, the vast complex polymorphic phase space remains largely unexplored, and the underlying microscopic mechanism for their phase transformations remains elusive. Here, we develop a highly accurate, efficient, and reliable machine-learning potential (MLP), which not only facilitates accurate exploration of the intricate potential energy surface (PES), but also enables us to conduct large-scale molecular dynamics (MD) simulations with first-principles accuracy. We identify the accurate structure of the $β''$ polymorph and uncover several previously unreported $β'$ polymorph variants exhibiting dynamic stability and competing energies, which are elucidated by characteristic flat imaginary phonon bands and the distinctive Mexican-hat-like PES in the $β$ polymorph. Through the MLP-accelerated MD simulations, we directly observe the polymorphic phase transformations among the $α$, $β$, $β'$, and $β''$ polymorphs under varying temperature and pressure conditions, and build for the first time an ab initio temperature-pressure phase diagram, showing good agreement with experiments. Furthermore, our MD simulations reveal a novel strain-induced reversible phase transition between the $β'$ and $β''$ polymorphs. This work not only unveils diverse polymorphs in van der Waals In$_2$Se$_3$, but also provides crucial atomic insights into their phase transitions, opening new avenues for the design of novel functional electronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs

This paper investigates the influence of cognitive biases on Large Language Models (LLMs) outputs. Cognitive biases, such as confirmation and availability biases, can distort user inputs through prompts, potentially leading to unfaithful and misleading outputs from LLMs. Using a systematic framework, our study introduces various cognitive biases into prompts and assesses their impact on LLM accuracy across multiple benchmark datasets, including general and financial Q&A scenarios. The results demonstrate that even subtle biases can significantly alter LLM answer choices, highlighting a critical need for bias-aware prompt design and mitigation strategy. Additionally, our attention weight analysis highlights how these biases can alter the internal decision-making processes of LLMs, affecting the attention distribution in ways that are associated with output inaccuracies. This research has implications for Al developers and users in enhancing the robustness and reliability of Al applications in diverse domains.

cs.CL↗

arXiv2025

$d$-Wave Flat Fermi Surface in Altermagnets Enables Maximum Charge-to-Spin Conversion

Altermagnets combine antiferromagnetic order with ferromagnet-like spin splitting, a duality that unlocks ultrafast spin-dependent responses. This unique property creates unprecedented opportunities for spin-current generation, overcoming the intrinsic limitations of conventional spin-transfer and spin-orbit torque approaches in magnetic memory technologies. Here, we establish a fundamental relationship between Fermi surface geometry and time-reversal-odd ($\mathcal{T}$-odd) spin currents in altermagnets through combined model analysis and first-principles calculations. We demonstrate that a $d$-wave altermagnet with a flat Fermi surface can achieve a theoretical upper limit of charge-to-spin conversion efficiency (CSE) of 100%. This mechanism is realized in the newly discovered room-temperature altermagnetic metal KV$_2$O$_2$Se, which exhibits a CSE of $\sim$78% at the charge neutrality point, nearly double that of RuO$_2$, setting a new record for $\mathcal{T}$-odd CSE. Under electron doping, this efficiency further increases to $\sim$98%, approaching the theoretical limit. Our work advances the fundamental understanding of $\mathcal{T}$-odd spin currents via Fermi surface geometry engineering and provides key insights for developing next-generation altermagnet-based memory devices.

cond-mat.mtrl-sci↗

arXiv2025

Nonlinear optical response in kagome lattice with inversion symmetry breaking

The kagome lattice is a fundamental model structure in condensed matter physics and materials science featuring symmetry-protected flat bands, saddle points, and Dirac points. This structure has emerged as an ideal platform for exploring various quantum physics. By combining effective model analysis and first-principles calculations, we propose that the synergy among inversion symmetry breaking, flat bands, and saddle point-related van Hove singularities within the kagome lattice holds significant potential for generating strong second-order nonlinear optical response. This property provides an inspiring insight into the practical application of the kagome-like materials, which is helpful for a comprehensive understanding of kagome lattice-related physics. Moreover, this work offers an alternative approach for designing materials with strong a second-order nonlinear optical response.

physics.optics↗

arXiv2025

Efficient moment tensor machine-learning interatomic potential for accurate description of defects in Ni-Al Alloys

Combining the efficiency of semi-empirical potentials with the accuracy of quantum mechanical methods, machine-learning interatomic potentials (MLIPs) have significantly advanced atomistic modeling in computational materials science and chemistry. This necessitates the continual development of MLIP models with improved accuracy and efficiency, which enable long-time scale molecular dynamics simulations to unveil the intricate underlying mechanisms that would otherwise remain elusive. Among various existing MLIP models, the moment tensor potential (MTP) model employs a highly descriptive rotationally-covariant moment tensor to describe the local atomic environment, enabling the use of even linear regression for model fitting. Although the current MTP model has achieved state-of-the-art efficiency for similar accuracy, there is still room for optimizing the contraction process of moment tensors. In this work, we propose an effective genetic algorithm based optimization scheme that can significantly reduce the number of independent moment tensor components and intermediate tensor components. This leads to a speedup of nearly one order of magnitude in efficiency and also improved accuracy compared to the traditional MTP model for intricate basis sets. We have applied our improved MTP model to predicting the energetic and dynamical properties of various point and planar defects in Ni-Al alloys, showing overall good performances and in general outperforming the semi-empirical potentials. This work paves the way for fast and accurate atomistic modeling of complex systems and provides a useful tool for modeling defects in Ni-Al alloys.

cond-mat.mtrl-sci↗

arXiv2025

Gravitationally Bound Gas Determines Star Formation in the Galaxy

Stars form from molecular gas under complex conditions influenced by multiple competing physical mechanisms, such as gravity, turbulence, and magnetic fields. However, accurately identifying the fraction of gas actively involved in star formation remains challenging. Using dust continuum observations from the Herschel Space Observatory, we derived column density maps and their associated probability distribution functions (N-PDFs). Assuming the power-law component in the N-PDFs corresponds to gravitationally bound (and thus star-forming) gas, we analyzed a diverse sample of molecular clouds spanning a wide range of mass and turbulence conditions. This sample included 21 molecular clouds from the solar neighborhood ($d<$500 pc) and 16 high-mass star-forming molecular clouds. For these two groups, we employed the counts of young stellar objects (YSOs) and mid-/far-infrared luminosities as proxies for star formation rates (SFR), respectively. Both groups revealed a tight linear correlation between the mass of gravitationally bound gas and the SFR, suggesting a universally constant star formation efficiency in the gravitationally bound gas phase. The star-forming gas mass derived from threshold column densities ($N_{\mbox {threshold}}$) varies from cloud to cloud and is widely distributed over the range of $\sim$1--17$\times$10$^{21}$ cm$^{-2}$ based on N-PDF analysis. But in solar neighborhood clouds, it is in rough consistency with the traditional approach using $A_{\rm V}$ $\ge$ 8 mag. In contrast, in high turbulent regions (e.g., the Central Molecular Zone) where the classical approach fails, the gravitationally bound gas mass and SFR still follow the same correlation as other high-mass star-forming regions in the Milky Way. Our findings also strongly support the interpretation that gas in the power-law component of the N-PDF is undergoing self-gravitational collapse to form stars.

astro-ph.GA↗

arXiv2025

Néel vector-dependent anomalous transport in altermagnetic metal CrSb

Altermagnets are predicted to exhibit anomalous transport phenomena, such as the anomalous Hall and Nernst effects, as observed in ferromagnets but with a vanishing net magnetic moment, akin to antiferromagnets. Despite their potential, progress has been limited due to the scarcity of metallic altermagnets. Motivated by the recent discovery of the altermagnetic metal CrSb, we conducted a systematic study of its electrical and thermoelectric transport properties, using first-principles calculations. CrSb exhibits low magnetocrystalline anisotropy energy, enabling the manipulation of the Néel vector in CrSb films through a suitable ferromagnetic substrate. The anomalous Hall and Nernst conductivities reach their maximum when the Néel vector is aligned along $\frac{1}{2}$\textbf{\textit{a}}+\textbf{\textit{b}}. The origins of both conductivities were analyzed in terms of Berry curvature distribution. Our results demonstrate that CrSb provides a good platform for investigating the Néel vector-dependent anomalous transport in altermagnetic metals.

cond-mat.mtrl-sci↗

arXiv2025

Gas Transfer Between the Inner 3-kpc Disk and the Galactic Central Molecular Zone

We uncovered a more tilted molecular gas structure with highly negative velocities located near the dust lane. Our observations also show that the approaching gas flows from the overshoot process are captured by the bar gravitational and then flows towards the Galactic central molecular zone (CMZ) through the bar channel. The recycling gas from the overshoot effect, in conjunction with freshly accreted gas from the inner 3-kpc disk, accumulates significantly near R_GC~1/2R_bar and R_GC~2/3R_bar regions by adopting a bar length of ~3.2--3.4kpc. Importantly, within these regions, there are frequent collisions and substantial angular momentum exchanges between gas flows with different trajectories. In this scenario, the DISSIPATION processes arising from interactions between colliding flows, together with the varying torques induced by the nonaxisymmetric bar, effectively transfer the angular momentum of viscous gas outward, thereby driving the molecular gas to settle into the CMZ within ~3 orbital periods. A long-term gas inflow with an average rate of >1.1Msun/yr, coupled with intense transient accretion events that exceed the average rate by several times due to the overshoot effect, significantly regulates the gas distribution, physical properties, and dynamical evolution of the CMZ. These findings provide robust observational evidence for elucidating the intricate dynamics of molecular gas flows towards the CMZ. Our results show that gas dynamics has a significant impact on the secular evolution of both the Milky Way and the extragalactic gas-rich galaxies.

astro-ph.GA↗

arXiv2025

Spin-to-charge conversion in orthorhombic RhSi topological semimetal crystalline thin films

The rise of non-magnetic topological semimetals, which provide a promising platform for observing and controlling various spin-orbit effects, has led to significant advancements in the field of topological spintronics. RhSi exists in two distinct polymorphs: cubic and orthorhombic crystal structures. The noncentrosymmetric B20 cubic structure has been extensively studied for hosting unconventional multifold fermions. In contrast, the orthorhombic structure, which crystallizes in the Pnma space group (No. 62), remains less explored and belongs to the family of topological Dirac semimetals. In this work, we investigate the structural, magnetic, and electrical properties of RhSi textured-epitaxial films grown on Si(111) substrates, which crystallize in the orthorhombic structure. We investigate the efficiency of pure spin current transport across RhSi/permalloy interfaces and the subsequent spin-to-charge current conversion via inverse spin Hall effect measurements. The xperimentally determined spin Hall conductivity in orthorhombic RhSi reaches a maximum value of 126 ($\hbar$/e)($Ω$.cm)$^{-1}$ at 10 K, which aligns reasonably well with first-principles calculations that attribute the spin Hall effect in RhSi to the spin Berry curvature mechanism. Additionally, we demonstrate the ability to achieve a sizable spin-mixing conductance (34.7 nm$^{-2}$) and an exceptionally high interfacial spin transparency of 88$%$ in this heterostructure, underlining its potential for spin-orbit torque switching applications. Overall, this study broadens the scope of topological spintronics, emphasizing the controlled interfacial spin-transport processes and subsequent spin-to-charge conversion in a previously unexplored topological Dirac semimetal RhSi/ferromagnet heterostructure.

cond-mat.mtrl-sci↗

arXiv2025

Decoding the Competing Effects of Dynamic Solvation Structures on Nuclear Magnetic Resonance Chemical Shifts of Battery Electrolytes via Machine Learning

Understanding the solvation structure of electrolytes is critical for optimizing the electrochemical performance of rechargeable batteries, as it directly influences properties such as ionic conductivity, viscosity, and electrochemical stability. The highly complex structures and strong interactions in high-concentration electrolytes make accurate modeling and interpretation of their ``structure-property" relationships even more challenging with spectroscopic methods. In this study, we present a machine learning-based approach to predict dynamic $^7$Li NMR chemical shifts in LiFSI/DME electrolyte solutions. Additionally, we provide a comprehensive structural analysis to interpret the observed chemical shift behavior in our experiments, particularly the abrupt changes in $^7$Li chemical shifts at high concentrations. Using advanced modeling techniques, we quantitatively establish the relationship between molecular structure and NMR spectra, offering critical insights into solvation structure assignments. Our findings reveal the coexistence of two competing local solvation structures that shift in dominance as electrolyte concentration approaches the concentrated limit, leading to anomalous reverse of $^7$Li NMR chemical shift in our experiment. This work provides a detailed molecular-level understanding of the intricate solvation structures probed by NMR spectroscopy, leading the way for enhanced electrolyte design.

physics.chem-ph↗

arXiv2025

Quantum Delocalization Enables Water Dissociation on Ru(0001)

We revisit the long-standing question of whether water molecules dissociate on the Ru(0001) surface through nanosecond-scale path-integral molecular dynamics simulations on a sizable supercell. This is made possible through the development of an efficient and reliable machine-learning potential with near first-principles accuracy, overcoming the limitations of previous ab initio studies. We show that the quantum delocalization associated with nuclear quantum effects enables rapid and frequent proton transfers between water molecules, thereby facilitating the water dissociation on Ru(0001). This work provides the direct theoretical evidence of water dissociation on Ru(0001), resolving the enduring issue in surface sciences and offering crucial atomistic insights into water-metal interfaces.

cond-mat.mtrl-sci↗

arXiv2025

Unsupervised Domain Adaptation with Dynamic Clustering and Contrastive Refinement for Gait Recognition

Gait recognition is an emerging identification technology that distinguishes individuals at long distances by analyzing individual walking patterns. Traditional techniques rely heavily on large-scale labeled datasets, which incurs high costs and significant labeling challenges. Recently, researchers have explored unsupervised gait recognition with clustering-based unsupervised domain adaptation methods and achieved notable success. However, these methods directly use pseudo-label generated by clustering and neglect pseudolabel noise caused by domain differences, which affects the effect of the model training process. To mitigate these issues, we proposed a novel model called GaitDCCR, which aims to reduce the influence of noisy pseudo labels on clustering and model training. Our approach can be divided into two main stages: clustering and training stage. In the clustering stage, we propose Dynamic Cluster Parameters (DCP) and Dynamic Weight Centroids (DWC) to improve the efficiency of clustering and obtain reliable cluster centroids. In the training stage, we employ the classical teacher-student structure and propose Confidence-based Pseudo-label Refinement (CPR) and Contrastive Teacher Module (CTM) to encourage noisy samples to converge towards clusters containing their true identities. Extensive experiments on public gait datasets have demonstrated that our simple and effective method significantly enhances the performance of unsupervised gait recognition, laying the foundation for its application in the real-world. We will release the code at https://github.com/YanSun-github/GaitDCCR upon acceptance.

cs.CV↗