arXiv subjects

Yu Song

Publications and source records attributed to Yu Song.

At least 55 records · Page 3Linked to original sources

arXiv2025

Anomalous Nernst Effect and Its Implications for Time-Reversal Symmetry Breaking in Kagome Metal ScV6Sn6

The nonmagnetic kagome metal ScV6Sn6 displays an unconventional charge order (CO) accompanied by signatures of an anomalous Hall effect, hidden magnetism, and multiple lattice instabilities. In this study, we report the observation of unconventional anomalous thermoelectric properties. Notably, unexpected anomalous transverse Nernst signals reach a peak value of ~4 μV/K near the TCDW ~92 K in ScV6Sn6, and these signals persist in the charge-ordered state as the temperature decreases to 10 K. Furthermore, both thermopower and thermal conductivity exhibit significant changes under magnetic fields, even in the nonmagnetic ground state. These observations strongly suggest the emergence of time-reversal symmetry breaking in ScV6Sn6, as supported by muon spin relaxation (μSR) measurements. While hidden magnetism represents the most plausible origin, alternative mechanisms involving orbital currents and chiral charge order remain possible.

cond-mat.str-el↗

arXiv2025

A Scalable Pretraining Framework for Link Prediction with Efficient Adaptation

Link Prediction (LP) is a critical task in graph machine learning. While Graph Neural Networks (GNNs) have significantly advanced LP performance recently, existing methods face key challenges including limited supervision from sparse connectivity, sensitivity to initialization, and poor generalization under distribution shifts. We explore pretraining as a solution to address these challenges. Unlike node classification, LP is inherently a pairwise task, which requires the integration of both node- and edge-level information. In this work, we present the first systematic study on the transferability of these distinct modules and propose a late fusion strategy to effectively combine their outputs for improved performance. To handle the diversity of pretraining data and avoid negative transfer, we introduce a Mixture-of-Experts (MoE) framework that captures distinct patterns in separate experts, facilitating seamless application of the pretrained model on diverse downstream datasets. For fast adaptation, we develop a parameter-efficient tuning strategy that allows the pretrained model to adapt to unseen datasets with minimal computational overhead. Experiments on 16 datasets across two domains demonstrate the effectiveness of our approach, achieving state-of-the-art performance on low-resource link prediction while obtaining competitive results compared to end-to-end trained methods, with over 10,000x lower computational overhead.

cs.LG↗

arXiv2025

ELMES: An Automated Framework for Evaluating Large Language Models in Educational Scenarios

The emergence of Large Language Models (LLMs) presents transformative opportunities for education, generating numerous novel application scenarios. However, significant challenges remain: evaluation metrics vary substantially across different educational scenarios, while many emerging scenarios lack appropriate assessment metrics. Current benchmarks predominantly measure general intelligence rather than pedagogical capabilities. To address this gap, we introduce ELMES, an open-source automated evaluation framework specifically designed for assessing LLMs in educational settings. ELMES features a modular architecture that enables researchers to create dynamic, multi-agent dialogues through simple configuration files, facilitating flexible scenario design without requiring extensive programming expertise. The framework incorporates a hybrid evaluation engine that objectively quantifies traditionally subjective pedagogical metrics using an LLM-as-a-Judge methodology. We conduct systematic benchmarking of state-of-the-art LLMs across four critical educational scenarios: Knowledge Point Explanation, Guided Problem-Solving Teaching, Interdisciplinary Lesson Plan Generation, and Contextualized Question Generation, employing fine-grained metrics developed in collaboration with education specialists. Our results demonstrate distinct capability distributions among models, revealing context-specific strengths and limitations. ELMES provides educators and researchers with an accessible evaluation framework that significantly reduces adaptation barriers for diverse educational applications while advancing the practical implementation of LLMs in pedagogy. The framework is publicly available at \emph{https://github.com/sii-research/elmes.git}.

cs.CY↗

arXiv2025

Reliable Transmission of LTP Using Reinforcement Learning-Based Adaptive FEC

Delay/Disruption Tolerant Networking (DTN) employs the Licklider Transmission Protocol (LTP) with Automatic Repeat reQuest (ARQ) for reliable data delivery in challenging interplanetary networks. While previous studies have integrated packet-level Forward Erasure Correction (FEC) into LTP to reduce retransmission time costs, existing static and delay-feedback-based dynamic coding methods struggle with highly variable and unpredictable deep space channel conditions. This paper proposes a reinforcement learning (RL)-based adaptive FEC algorithm to address these limitations. The algorithm utilizes historical feedback and system state to predict future channel conditions and proactively adjust the code rate. This approach aims to anticipate channel quality degradation, thereby preventing decoding failures and subsequent LTP retransmissions and improving coding efficiency by minimizing redundancy during favorable channel conditions. Performance evaluations conducted in simulated Earth-Moon and Earth-Mars link scenarios demonstrate this algorithm's effectiveness in optimizing data transmission for interplanetary networks. Compared to existing methods, this approach demonstrates significant improvement, with matrix decoding failures reduced by at least 2/3.

cs.NI↗

arXiv2025

Mechanism of $E'_γ$ Defect Generation in Ionizing-irradiated $a$-SiO$_2$: The Nonradiative Carrier Capture-Structural Relaxation Model

The total ionizing dose (TID) effect of semiconductor devices stems from radiation-induced $E'_γ$ defects in the $a$-SiO$_2$ dielectrics, but the conventional ``hole transport-trapping'' model of defect generation fails to explain recent basic experiments. Here, we propose an essentially new ``nonradiative carrier capture-structural relaxation'' (NCCSR) mechanism that can consistently explain the puzzling temperature/electric-field dependence, based on spin-polarized HSE06 hybrid functional calculations and existing experimental alignment of defect formation energies and charge capture cross-sections of large-sample oxygen vacancies in $a$-SiO$_2$. It is revealed that, the long-assumed $V_{Oγ}$ precursors with high formation energy cannot survive in high temperature-grown $a$-SiO$_2$; whereas the stable $V_{Oδ}$ can capture irradiation-induced holes via strong electron-phonon coupling, generating metastable $E'_δ$ that most relax into stable $E'_γ$. A fractional power-law (FPL) dynamic model is derived based on the mechanism and the Kohlrausch-Williams Watts (KWW) decay function. It can uniformly describe nonlinear data over a wide dose and temperature range. This work not only provides a solid cornerstone for prediction and hardening of TID effects of SiO$_2$-based semiconductor devices, but also offers a general approach for studying ionizing radiation physics in alternative dielectrics with intrinsic electronic metastability and dispersion.

cond-mat.mtrl-sci↗

arXiv2025

Higher-order Structure Boosts Link Prediction on Temporal Graphs

Temporal Graph Neural Networks (TGNNs) have gained growing attention for modeling and predicting structures in temporal graphs. However, existing TGNNs primarily focus on pairwise interactions while overlooking higher-order structures that are integral to link formation and evolution in real-world temporal graphs. Meanwhile, these models often suffer from efficiency bottlenecks, further limiting their expressive power. To tackle these challenges, we propose a Higher-order structure Temporal Graph Neural Network, which incorporates hypergraph representations into temporal graph learning. In particular, we develop an algorithm to identify the underlying higher-order structures, enhancing the model's ability to capture the group interactions. Furthermore, by aggregating multiple edge features into hyperedge representations, HTGN effectively reduces memory cost during training. We theoretically demonstrate the enhanced expressiveness of our approach and validate its effectiveness and efficiency through extensive experiments on various real-world temporal graphs. Experimental results show that HTGN achieves superior performance on dynamic link prediction while reducing memory costs by up to 50\% compared to existing methods.

cs.LG↗

arXiv2025

Linear scaling relation between two-dimensional massless Dirac fermion Fermi velocity and Fe-As bond length in iron arsenide superconductor systems

Two-dimensional (2D) massless Dirac fermions (MDF), which represent a type of quasi-particles with linear energy-momentum dispersions only in 2D momentum space, provide a fertile ground for realizing novel quantum phenomena. However, 2D MDF were seldom observed in the superconducting bulk states of 3D materials. Furthermore, as a cornerstone for accurately tuning the quantum phenomena based on 2D MDF, a quantitative relationship between 2D MDF and a structural parameter has rarely been revealed so far. Here, we report magneto-infrared spectroscopy studies of the iron-arsenide-superconductor systems NaFeAs and $A\mathrm{Fe_2As_2} (A = \mathrm{Ca, Ba})$ at temperature $T \sim 4.2 $ K and at magnetic fields ($B$) up to 17.5 T. Our results demonstrate the existence of 2D MDF in the superconducting bulk state of NaFeAs. Moreover, the 2D-MDF Fermi velocities in NaFeAs and $A\mathrm{Fe_2As_2} (A = \mathrm{Ca, Ba})$, which are extracted from the slopes of the linear $\sqrt{B}$ dependences of the Landau-level transition energies, scale linearly with the Fe-As bond lengths. The linear scaling between the 2D-MDF Fermi velocities and the Fe-As bond lengths is supported by (i) the linear relationship between the square root of the effective mass of the $d_{xy}$ electrons and the Fe-As bond length and (ii) the linear dependence of the square root of the calculated tight-binding hopping energy on the Fe-As bond length. Our results open up new avenues for exploring and tuning novel quantum phenomena based on 2D MDF in the superconducting bulk states of 3D materials.

cond-mat.supr-con↗

arXiv2025

$3d$ flat bands and coupled $4f$ moments in the kagome-honeycomb permanent magnet Sm$_{2}$Co$_{17}$

Rare earth permanent magnets (REPMs) with both localized moments and itinerant conduction bands are not only important for fundamental research but also have significant technological applications. In particular, Sm$_{\rm 2}$Co$_{\rm 17}$ is a prototypical high-temperture REPM, where the Co atoms form a kagome-honeycomb stacked lattice. Here we report synthesis of epitaxial Sm$_{\rm 2}$Co$_{\rm 17}$ films using molecular beam epitaxy and measurements of their momentum-resolved electronic structure from \textit{in-situ} angle-resolved photoemission spectroscopy. Our results unveil two flat bands from Co $3d$ orbitals near the Fermi level ($E_F$), one at $\sim$\,--300\,meV and another right at $E_F$, which arise from orbital-selective destructive interference and strong electron correlations, respectively. In addition, our results reveal that Sm $4f$ states are far away from $E_F$ (hence mostly localized) and exhibit an anomalous temperature dependence, caused by the $3d$-$4f$ magnetic coupling. Our findings provide direct spectroscopic insights to understand the strong uniaxial ferromagnetism in Sm$_{\rm 2}$Co$_{\rm 17}$ (and REPMs alike). Our work also opens avenues to explore flat-band physics near $E_F$ and emergent phenomena in correlated kagome-honeycomb lattices.

cond-mat.str-el↗

arXiv2025

Accurate Tracking of Arabidopsis Root Cortex Cell Nuclei in 3D Time-Lapse Microscopy Images Based on Genetic Algorithm

Arabidopsis is a widely used model plant to gain basic knowledge on plant physiology and development. Live imaging is an important technique to visualize and quantify elemental processes in plant development. To uncover novel theories underlying plant growth and cell division, accurate cell tracking on live imaging is of utmost importance. The commonly used cell tracking software, TrackMate, adopts tracking-by-detection fashion, which applies Laplacian of Gaussian (LoG) for blob detection, and Linear Assignment Problem (LAP) tracker for tracking. However, they do not perform sufficiently when cells are densely arranged. To alleviate the problems mentioned above, we propose an accurate tracking method based on Genetic algorithm (GA) using knowledge of Arabidopsis root cellular patterns and spatial relationship among volumes. Our method can be described as a coarse-to-fine method, in which we first conducted relatively easy line-level tracking of cell nuclei, then performed complicated nuclear tracking based on known linear arrangement of cell files and their spatial relationship between nuclei. Our method has been evaluated on a long-time live imaging dataset of Arabidopsis root tips, and with minor manual rectification, it accurately tracks nuclei. To the best of our knowledge, this research represents the first successful attempt to address a long-standing problem in the field of time-lapse microscopy in the root meristem by proposing an accurate tracking method for Arabidopsis root nuclei.

cs.CV↗

arXiv2025

Polarization Patterns of the Hotspots Plunging into a Kerr Black Hole

The multi-wavelength polarized light signals from supermassive black holes have sparked many studies on polarized images of accretion disks and hotspots. However, the polarization patterns within the innermost stable circular orbit (ISCO) region remain to be explored. In this study, we focus on two specific types of orbits, namely the plunging geodesics inward from the ISCO and homoclinic geodesics, to uncover the polarization features associated with non-circular motion in a Kerr spacetime. For an on-axis observer, we specifically develop an approximate function to describe gravitational lensing along the azimuthal direction and establish a simplified synchrotron emission model. Based on these, we analyze the time-integrated polarized images of hotspots and their Stokes parameters. Moreover, we explore the polarized image of the plunging region within a thin accretion disk.

astro-ph.HE↗

arXiv2025

Emergent ferromagnetic ladder excitations in heavy fermion superconductor CeSb$_{2}$

Low-dimensional spin fluctuations play a crucial role in unconventional superconductors, with quasi-one-dimensional spin excitations potentially linked with spin-triplet superconductivity. The heavy fermion superconductor CeSb$_2$ exhibits an unusual large inverted S-shaped upper critical field that suggests a possible triplet pairing state within its pressure-induced superconducting dome. Using inelastic neutron scattering, we discover quasi-one-dimensional magnetic excitations in CeSb$_2$ emerging from nearly square Ce layers with minor orthorhombic deformation. We show that the data are well described by a ferromagnetic spin ladder model, where the "rungs" of the ladder straddle Ce bilayers. Moreover, we find that diffuse excitations akin to those in the ordered phase persist well above $T_{\rm N}$, suggesting that quasi-one-dimensional ferromagnetic paramagnons may significantly contribute to the unusual superconductivity that appears under pressure once magnetic order is suppressed.

cond-mat.supr-con↗

arXiv2025

Early signs of stuck pipe detection based on Crossformer

Stuck pipe incidents are one of the major challenges in drilling engineering,leading to massive time loss and additional costs.To address the limitations of insufficient long sequence modeling capability,the difficulty in accurately establishing warning threshold,and the lack of model interpretability in existing methods,we utilize Crossformer for early signs of detection indicating potential stuck events in order to provide guidance for on-site drilling engineers and prevent stuck pipe incidents.The sliding window technique is integrated into Crossformer to allow it to output and display longer outputs,the improved Crossformer model is trained using normal time series drilling data to generate predictions for various parameters at each time step.The relative reconstruction error of model is regard as the risk of stuck pipe,thereby considering data that the model can't predict as anomalies,which represent the early signs of stuck pipe incidents.The multi-step prediction capability of Crossformer and relative reconstruction error are combined to assess stuck pipe risk at each time step in advance.We partition the reconstruction error into modeling error and error due to anomalous data fluctuations,furthermore,the dynamic warning threshold and warning time for stuck pipe incidents are determined using the probability density function of reconstruction errors from normal drilling data.The results indicate that our method can effectively detect early signs of stuck pipe incidents during the drilling process.Crossformer exhibits superior modeling and predictive capabilities compared with other deep learning models.Transformer-based models with multi-step prediction capability are more suitable for stuck pipe prediction compared to the current single-step prediction models.

cs.CE↗

arXiv2025

Image Pre-Processing Framework for Time-Domain Astronomy in the Artificial Intelligence Era

The rapid advancement of image analysis methods in time-domain astronomy, particularly those leveraging AI algorithms, has highlighted efficient image pre-processing as a critical bottleneck affecting algorithm performance. Image pre-processing, which involves standardizing images for training or deployment of various AI algorithms, encompasses essential steps such as image quality evaluation, alignment, stacking, background extraction, gray-scale transformation, cropping, source detection, astrometry, and photometry. Historically, these algorithms were developed independently by different research groups, primarily based on CPU architecture for small-scale data processing. This paper introduces a novel framework for image pre-processing that integrates key algorithms specifically modified for GPU architecture, enabling large-scale image pre-processing for different algorithms. To prepare for the new algorithm design paradigm in the AI era, we have implemented two operational modes in the framework for different application scenarios: Eager mode and Pipeline mode. The Eager mode facilitates real-time feedback and flexible adjustments, which could be used for parameter tuning and algorithm development. The pipeline mode is primarily designed for large scale data processing, which could be used for training or deploying of artificial intelligence models. We have tested the performance of our framework using simulated and real observation images. Results demonstrate that our framework significantly enhances image pre-processing speed while maintaining accuracy levels comparable to CPU based algorithms. To promote accessibility and ease of use, a Docker version of our framework is available for download in the PaperData Repository powered by China-VO, compatible with various AI algorithms developed for time-domain astronomy research.

astro-ph.IM↗

arXiv2025

Three-dimensional mapping of the altermagnetic spin splitting in CrSb

Altermagnetism, a kind of collinear magnetism that is characterized by a momentum-dependent band and spin splitting without net magnetization, has recently attracted considerable interest. Finding altermagnetic materials with large splitting near the Fermi level necessarily requires three-dimensional k-space mapping. While this is crucial for spintronic applications and emergent phenomena, it remains challenging. Here, using synchrotron-based angle-resolved photoemission spectroscopy (ARPES), spin-resolved ARPES and model calculations, we uncover a large altermagnetic splitting, up to ~1.0 eV, near the Fermi level in CrSb. We verify its bulk-type g-wave altermagnetism through systematic three-dimensional k-space mapping, which unambiguously reveals the altermagnetic symmetry and associated nodal planes. Spin-resolved ARPES measurements further verify the spin polarizations of the split bands near Fermi level. Tight-binding model analysis indicates that the large altermagnetic splitting arises from strong third-nearest-neighbor hopping mediated by Sb ions. The large band/spin splitting near Fermi level in metallic CrSb, together with its high TN (up to 705 K) and simple spin configuration, paves the way for exploring emergent phenomena and spintronic applications based on altermagnets.

cond-mat.mtrl-sci↗

arXiv2025

Structural and Physical Properties of the Heavy Fermion Metal Ce$_2$NiAl$_6$Si$_5$

Strongly correlated electrons at the verge of quantum criticality give rise to unconventional phases of matter and behaviors, with the discovery of new quantum critical materials driving synergistic advances in both experiments and theory. In this work, we report the structural and physical properties of a new quaternary Ce-based heavy fermion compound, Ce$_2$NiAl$_6$Si$_5$, synthesized using the self-flux method. This compound forms a layered tetragonal structure (space group $P4/nmm$), with square nets of Ce atoms separated by Si-Al or Ni-Si-Ge layers. Specific heat measurements show a low temperature Sommerfeld coefficient of 1.4 J/mol-Ce K$^{2}$, with a reduced entropy indicative of significant Kondo interactions. Below 0.6 K, an upturn in resistivity and a deviation in magnetic susceptibility suggest the appearance of magnetic ordering or the development of dynamic magnetic correlations, which is further supported by a bulge in specific heat around 0.4 K. These results suggest that Ce$_2$NiAl$_6$Si$_5$ is a layered heavy fermion metal, naturally located in proximity to a spin-density-wave quantum critical point.

cond-mat.str-el↗

arXiv2024

Quasi-two-dimensional Antiferromagnetic Spin Fluctuations in the Spin-triplet Superconductor Candidate CeRh$_2$As$_2$

The tetragonal heavy-fermion superconductor CeRh$_2$As$_2$ ($T_{\rm c}=0.3$ K) exhibits an exceptionally high critical field of 14 T for $\textbf{B} \parallel \textbf{c}$. It undergoes a field-driven first-order phase transition between superconducting (SC) states, potentially transitioning from spin-singlet to spin-triplet superconductivity. To elucidate the underlying pairing mechanism, we probe spin fluctuations in CeRh$_2$As$_2$ using neutron scattering. We find dynamic $(π,π)$ antiferromagnetic spin correlations with an anisotropic quasi-two-dimensional correlation volume. Our data place an upper limit of 0.31 $μ_{\rm B}$ on the staggered magnetization of corresponding Néel orders at $T=0.08$ K. Density functional theory (DFT) calculations, treating Ce $4f$ electrons as core states, show that the AFM wave vector connects significant areas of the Fermi surface. Our findings show the dominant excitations in CeRh$_2$As$_2$ for $\hbarω< 1.2$~meV are magnetic and indicate superconductivity in CeRh$_2$As$_2$ is mediated by AFM spin fluctuations associated with a proximate quantum critical point.

cond-mat.str-el↗

arXiv2024

Charge Density Fluctuations with Enhanced Superconductivity at the Proposed Nematic Quantum Critical Point

A quantum critical point (QCP) represents a continuous phase transition at absolute zero. At the QCP of an unconventional superconductor, enhanced superconducting transition temperature and magnetic fluctuations strength are often observed together, indicating magnetism-mediated superconductivity. This raises the question of whether quantum fluctuations in other degrees of freedom, such as charge, could similarly boost superconductivity. However, because charge is frequently intertwined with magnetism, isolating and understanding its specific role in Cooper pair formation poses a significant challenge. Here, we report persistent charge density fluctuations (CDF) down to 15 K in the non-magnetic superconductor Sr$_{0.77}$Ba$_{0.23}$Ni$_{2}$As$_{2}$, which lies near a proposed nematic QCP associated with a six-fold enhancement of superconductivity. Our results show that the quasi-elastic CDF does not condense into resolution-limited Bragg peaks and displays non-saturated strength. The CDF completely softens at 25 K, with its critical behavior described by the same mathematical framework as the antiferromagnetic Fermi liquid model, yielding a fitted Curie-Weiss temperature of $θ\approx 0$ K. Additionally, we find that the nematic fluctuations are not lattice-driven, as evidenced by the absence of softening in nematic-coupled in-plane transverse acoustic phonons. Our discovery positions Sr$_{x}$Ba$_{1-x}$Ni$_{2}$As$_{2}$ as a promising candidate for charge-fluctuation-driven nematicity and superconductivity.

cond-mat.supr-con↗

arXiv2024

Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding

Brain-computer interfaces (BCIs) are an advanced fusion of neuroscience and artificial intelligence, requiring stable and long-term decoding of neural signals. Spiking Neural Networks (SNNs), with their neuronal dynamics and spike-based signal processing, are inherently well-suited for this task. This paper presents a novel approach utilizing a Multiscale Fusion enhanced Spiking Neural Network (MFSNN). The MFSNN emulates the parallel processing and multiscale feature fusion seen in human visual perception to enable real-time, efficient, and energy-conserving neural signal decoding. Initially, the MFSNN employs temporal convolutional networks and channel attention mechanisms to extract spatiotemporal features from raw data. It then enhances decoding performance by integrating these features through skip connections. Additionally, the MFSNN improves generalizability and robustness in cross-day signal decoding through mini-batch supervised generalization learning. In two benchmark invasive BCI paradigms, including the single-hand grasp-and-touch and center-and-out reach tasks, the MFSNN surpasses traditional artificial neural network methods, such as MLP and GRU, in both accuracy and computational efficiency. Moreover, the MFSNN's multiscale feature fusion framework is well-suited for the implementation on neuromorphic chips, offering an energy-efficient solution for online decoding of invasive BCI signals.

cs.NE↗